Week 4 - Testing, Security & Privacy : Copilot Free learning & cert prep

[Akash1134]

👋 Welcome to the final week of the GitHub Copilot Free learning journey and cert prep!

Over the past three weeks, we’ve learned a lot together—exploring Copilot, tackling super-engaging challenges, and gathering valuable feedback from all of you. It’s been amazing to see how these resources have helped shape your learning curve and exam prep!

Now, as we enter this last stretch, let’s make it count. This week is all about wrapping up strong, reinforcing key learnings, and getting you ready to ace that certification (if you're opting for one). Here’s what’s coming up:

• Testing with GitHub Copilot
  • Options for generating testing for your code
    • Enhance code quality through testing
    • Leverage GitHub Copilot for security and performance
  • Privacy fundamentals and context exclusions
    • Describe the different SKUs for GitHub Copilot
    • Identify content exclusions
    • Safeguards and Troubleshooting

If you’d like a recap of what we’ve covered throughout this series, see the detailed study guide.

Need a quick refresher or want to look back into earlier lessons?

• Week 1: Getting started! 🔢
• Week 2: Features & Data handling
• Week 3: Prompt Engineering & Productivity

Your First Move: Study Smart, Level Up! 🎯

• Microsoft Learn Modules (each of the modules has its own knowledge check exercise)
  • Develop unit tests using GitHub Copilot tools
  • Implement code improvements using GitHub Copilot tools
  • Guided project - Accelerate app development using GitHub Copilot tools
  • Management and customization considerations with GitHub Copilot
• GitHub Learning Pathways
  • Essentials of GitHub Copilot (series)
• Videos
  • GitHub Copilot Study Guide Series 3: Testing with GitHub Copilot
  • Test like a pro with Playwright and GitHub Copilot
  • Write better tests and stop breaking the build
  • Getting Started with Debugging in VS Code
  • Ways You're Not Using Copilot to Its Full Potential
  • Write Tests using GitHub Copilot Chat
  • Getting started with GitHub Copilot (Playlist with over 80 videos)
  • NEW: GitHub Copilot Tutorial with Visual Studio Code
  • NEW: Walkthrough for Everyone on Using the GitHub Copilot Free Offer
• Blogs and Resources
  • GitHub Copilot Trust Center
  • How GitHub Copilot is getting better at understanding your code
  • How to use GitHub Copilot: Prompts, tips, and use cases
  • How to responsibly adopt GitHub Copilot with the GitHub Copilot Trust Center
  • NEW: Introducing GitHub Copilot Agent Mode (Preview)
  • NEW: GitHub Copilot for Azure DevOps Users
• GitHub Docs
  • Subscription plans for GitHub Copilot (SKU’s)
  • Testing code
  • Writing tests with GitHub Copilot
  • Finding existing vulnerabilities in code
  • Configuring and auditing content exclusion
  • Troubleshooting GitHub Copilot
  • Use GitHub Copilot
  • Manage GitHub Copilot

Note

Here’s your friendly nudge: don’t forget—top participants will snag a GitHub Certifications exam voucher! 🎟️ and what’s more rewarding than finishing strong in the final week of this learning journey! Make this week count! 🚀

Knowledge Checkpoint 🏁 - Let’s See What You’ve Got! 🧠

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?

(Choose two correct answers.)

A) Rely on Copilot’s first test suggestion and refine manually
B) Provide explicit comments describing edge case scenarios before writing the function
C) Use a combination of property-based testing and manually crafted assertions
D) Trust Copilot’s completion and execute tests without modifications

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

A) Ignore the test case and generate a new one
B) Modify assertions to account for precision tolerance
C) Use Copilot Chat to rewrite the function with better numerical stability
D) Disable Copilot when dealing with floating-point calculations

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

A) Write failing test cases before implementing the function
B) Let Copilot generate tests after writing the function
C) Use Copilot Chat to refactor existing tests post-development
D) Enable Copilot’s TDD mode

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

A) Using Copilot without an internet connection
B) Providing incomplete function definitions without parameter types
C) Using Copilot with multiple test frameworks in the same file
D) Asking Copilot to generate tests for functions written in a different programming language

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?

(Choose two correct answers.)

A) Asking Copilot Chat to explain unexpected test failures
B) Using Copilot Chat to automatically resolve compilation errors
C) Providing error logs to Copilot Chat for step-by-step troubleshooting
D) Relying entirely on Copilot’s debugging suggestions without verification

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

A) Manually modify Copilot’s output to include large payloads
B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
C) Enable Copilot’s ‘large dataset’ mode in settings
D) Increase the timeout value for Copilot to process larger requests

7. What is the primary function of GitHub Copilot’s content exclusion settings?

A) Prevent Copilot from accessing sensitive internal documentation
B) Restrict Copilot from suggesting code similar to private repositories
C) Block Copilot from using specific types of data when generating suggestions
D) Limit Copilot’s ability to suggest insecure code patterns

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?

(Choose two correct answers.)

A) When Copilot is used without context-aware suggestions
B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
C) When Copilot is used in a private repository with strict security settings
D) When Copilot generates suggestions based on similar insecure patterns from public repositories

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

A) Increase the number of test cases Copilot generates
B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
C) Use Copilot Chat to ask for ‘comprehensive financial validation tests’
D) Modify Copilot’s configuration to generate stricter tests

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

A) Add @async as an annotation
B) Provide comments specifying ‘test async functions’
C) Manually modify Copilot-generated test cases
D) Change Jest’s settings to enable async mode

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

A) Copilot is not trained to write test cases
B) Copilot only generates tests when a function is explicitly documented
C) Copilot is missing necessary function comments or contextual prompts
D) The test framework is not supported by Copilot

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

A) GitHub Copilot Individual
B) GitHub Copilot for Business
C) GitHub Copilot Enterprise
D) GitHub Copilot Pro

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

A) Specify ‘Use secure cryptographic methods’ in comments
B) Copy Copilot’s first suggestion and manually verify security
C) Disable Copilot when writing encryption functions
D) Enable Copilot’s security-enhanced mode

14. You have a function handling user authentication, and Copilot generates the following test case:

def test_authenticate_user():
    assert authenticate("admin", "password123") == True

What is the most critical issue with this test?

A) The test lacks edge cases
B) It uses hardcoded credentials, which is a security risk
C) Copilot should generate negative test cases as well
D) All of the above

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?

(Choose two correct answers.)

A) Copilot automatically detects API keys and removes them
B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
C) Copilot flags insecure patterns but does not block suggestions
D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials

Share your reactions 🚀 below or in the comment section.

[Ronaldo-001]

Lezz go!!

[golh30]

Let's join.

[johnniekpng]

Go go go

[paulsuv9]

Lets go!!! 🚀🚀🚀

[mvark]

Many words contain silent or extra letters that make pronunciation tricky. If your native language follows phonetic spelling (where words are pronounced as they are written), you might instinctively apply the same logic to English. This may lead to unexpected mistakes - like pronouncing the “t” in ballet or being puzzled by why colonel is pronounced ker-nil.

To help with this, I started compiling a list of such tricky words in a Google Spreadsheet. Then, I learned that Google Sheets can function as a read-only database, so I built a simple web app using the Google Sheets API and JavaScript to display the constantly updated list online.

Recently, I discovered Datasette, a fantastic open-source tool that converts CSV files into SQLite database tables, and also allows you to browse, view, facet, filter, share, and explore data.

Datasette also exposes the contents of a CSV file as a JSON API. I wanted to adapt my original app, which relied on Google Sheets, to use this capability. I downloaded the list as a CSV file and uploaded it to Datasette, which I self-hosted on Glitch. Glitch.com is a platform that lets you create, remix, and host web apps without worrying about servers or complex setup.

Instead of coding everything from scratch, I used GitHub Copilot inside Visual Studio Code.

For the Prompt battle that was announced last week, I wanted to start with a very basic prompt and expected to refine based on its output.

To my amazement, with just this prompt to the Claude 3.5 Sonnet option in Copilot Chat, it gave me a working sample 🤯 -

Use this JSON endpoint: https://rightful-veiled-lyr.glitch.me/data.json?sql=select+word%2C+pronunciation+from+aphonetic and display the key-value pairs on a web page.

I didn't have to refine, it read my mind yet again with a prompt of about 10 words!

For my hobby apps, I like styling to be "minimal but functional" and it generated code in that fashion.

Try my PhonaTick app, check the code of the third app in my WebApps collection and let me know what you think!

Besides experiencing the magical powers of GitHub Copilot, the other big thing I learnt was how simple it was to host a JSON API with the open-source Datasette tool.

[paulsuv9]

Your transition from Google Sheets to Datasette is truly impressive! 🌟 It’s incredible how accurately Copilot understood your needs with such a simple prompt. I’m excited to explore PhonaTick and your WebApps collection. Thanks for sharing your journey! 🚀

[mvpkenlin]

Let's go

[kalharatennakoon]

Hey @Akash1134 👋,
Here are my answers.

1. B & C
2. B
3. A
4. B
5. A & C
6. B
7. B
8. B & D
9. B
10. B
11. C
12. C
13. A
14. D
15. A & B

[Ronaldo-001]

Thanks @Akash1134 for providing these amazing resources !

Here are my answers:

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?

(Choose two correct answers.)

• ❌ A) Rely on Copilot’s first test suggestion and refine manually
• ✅ B) Provide explicit comments describing edge case scenarios before writing the function
• ✅ C) Use a combination of property-based testing and manually crafted assertions
• ❌ D) Trust Copilot’s completion and execute tests without modifications

Reason: Explicitly defining edge cases helps Copilot generate more comprehensive tests. Property-based testing ensures broader coverage beyond standard unit tests.

---

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

• ❌ A) Ignore the test case and generate a new one
• ✅ B) Modify assertions to account for precision tolerance
• ❌ C) Use Copilot Chat to rewrite the function with better numerical stability
• ❌ D) Disable Copilot when dealing with floating-point calculations

Reason: Floating-point precision issues are common; using assertions with tolerance helps resolve them effectively.

---

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

• ✅ A) Write failing test cases before implementing the function
• ❌ B) Let Copilot generate tests after writing the function
• ❌ C) Use Copilot Chat to refactor existing tests post-development
• ❌ D) Enable Copilot’s TDD mode

Reason: TDD requires writing failing tests first, then implementing the function to make them pass.

---

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

• ❌ A) Using Copilot without an internet connection
• ✅ B) Providing incomplete function definitions without parameter types
• ❌ C) Using Copilot with multiple test frameworks in the same file
• ❌ D) Asking Copilot to generate tests for functions written in a different programming language

Reason: Lack of type definitions can lead to inaccurate test suggestions.

---

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?

(Choose two correct answers.)

• ✅ A) Asking Copilot Chat to explain unexpected test failures
• ❌ B) Using Copilot Chat to automatically resolve compilation errors
• ✅ C) Providing error logs to Copilot Chat for step-by-step troubleshooting
• ❌ D) Relying entirely on Copilot’s debugging suggestions without verification

Reason: Understanding test failures and providing error logs help debug effectively, but manual verification is essential.

---

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

• ❌ A) Manually modify Copilot’s output to include large payloads
• ✅ B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
• ❌ C) Enable Copilot’s ‘large dataset’ mode in settings
• ❌ D) Increase the timeout value for Copilot to process larger requests

Reason: Providing specific instructions in comments helps Copilot generate more relevant test cases.

---

7. What is the primary function of GitHub Copilot’s content exclusion settings?

• ❌ A) Prevent Copilot from accessing sensitive internal documentation
• ✅ B) Restrict Copilot from suggesting code similar to private repositories
• ❌ C) Block Copilot from using specific types of data when generating suggestions
• ❌ D) Limit Copilot’s ability to suggest insecure code patterns

Reason: Content exclusion prevents Copilot from referencing private/internal data when generating suggestions.

---

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?

• ✅ A) When Copilot is used without context-aware suggestions
• ❌ B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
• ❌ C) When Copilot is used in a private repository with strict security settings
• ❌ D) When Copilot generates suggestions based on similar insecure patterns from public repositories

Reason: Without context-aware suggestions, Copilot may generate insecure code.

---

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

• ❌ A) Increase the number of test cases Copilot generates
• ✅ B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
• ❌ C) Use Copilot Chat to ask for ‘comprehensive financial validation tests’
• ❌ D) Modify Copilot’s configuration to generate stricter tests

Reason: Clearly specifying edge cases helps Copilot cover scenarios like integer overflows.

---

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

• ❌ A) Add @async as an annotation
• ✅ B) Provide comments specifying ‘test async functions’
• ❌ C) Manually modify Copilot-generated test cases
• ❌ D) Change Jest’s settings to enable async mode

Reason: Explicit instructions help Copilot generate async test cases correctly.

---

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

• ❌ A) Copilot is not trained to write test cases
• ❌ B) Copilot only generates tests when a function is explicitly documented
• ✅ C) Copilot is missing necessary function comments or contextual prompts
• ❌ D) The test framework is not supported by Copilot

Reason: Lack of proper context can lead to irrelevant test suggestions.

---

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

• ❌ A) GitHub Copilot Individual
• ❌ B) GitHub Copilot for Business
• ✅ C) GitHub Copilot Enterprise
• ❌ D) GitHub Copilot Pro

Reason: Only the Enterprise SKU provides organization-wide content exclusion settings.

---

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

• ✅ A) Specify ‘Use secure cryptographic methods’ in comments
• ❌ B) Copy Copilot’s first suggestion and manually verify security
• ❌ C) Disable Copilot when writing encryption functions
• ❌ D) Enable Copilot’s security-enhanced mode

Reason: Providing security-focused instructions helps ensure better cryptographic implementations.

---

14. You have a function handling user authentication, and Copilot generates the following test case:

def test_authenticate_user():
    assert authenticate("admin", "password123") == True

What is the most critical issue with this test?

• ❌ A) The test lacks edge cases
• ❌ B) It uses hardcoded credentials, which is a security risk
• ❌ C) Copilot should generate negative test cases as well
• ✅ D) All of the above

Reason: Hardcoded credentials are a major security risk, and lack of negative/edge cases weakens the test.

---

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?

• ✅ A) Copilot automatically detects API keys and removes them
• ✅ B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
• ❌ C) Copilot flags insecure patterns but does not block suggestions
• ❌ D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials
  Reason: Copilot proactively removes sensitive data and respects repo secret settings.

This week really tested me and was also really interesting!

[paulsuv9]

Thanks for sharing your quiz answers and reasoning! 🧠 Your detailed explanations are very helpful. Have you encountered any other interesting challenges while using Copilot for testing? 🚀

[SatyamSharma007]

Lets go

[RAVINDRA8008]

Excited for the final stretch! 🚀 The journey with GitHub Copilot has been amazing, and this last week looks packed with valuable insights. Ready to dive into testing, security, and performance optimization! Let’s finish strong! 💪🔥 #GitHubCopilot #LearningJourney

[paulsuv9]

Absolutely, @RAVINDRA8008! This final week is crucial. I’m particularly excited about the security enhancements. What’s been your favorite feature so far? Let’s keep the momentum going! 🚀

[RAVINDRA8008]

This has been an incredible learning experience! 🎉 Looking forward to refining my testing skills and making the most of GitHub Copilot. Who else is excited for the final week? Let’s ace this together! 💻✅ #CopilotCertPrep

[paulsuv9]

Totally agree! This journey has been so enriching. I’m excited to apply all these new skills in my projects. What’s one thing you’ve mastered this week? 🚀

[SatyamSharma007]

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?
   ✅ B) Provide explicit comments describing edge case scenarios before writing the function
   ✅ C) Use a combination of property-based testing and manually crafted assertions

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?
   ✅ B) Modify assertions to account for precision tolerance

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?
   ✅ A) Write failing test cases before implementing the function

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?
   ✅ B) Providing incomplete function definitions without parameter types

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?
   ✅ A) Asking Copilot Chat to explain unexpected test failures
   ✅ C) Providing error logs to Copilot Chat for step-by-step troubleshooting

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?
   ✅ B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot

7. What is the primary function of GitHub Copilot’s content exclusion settings?
   ✅ B) Restrict Copilot from suggesting code similar to private repositories

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?
   ✅ A) When Copilot is used without context-aware suggestions

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?
   ✅ B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?
    ✅ B) Provide comments specifying ‘test async functions’

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?
    ✅ C) Copilot is missing necessary function comments or contextual prompts

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?
    ✅ C) GitHub Copilot Enterprise

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?
    ✅ A) Specify ‘Use secure cryptographic methods’ in comments

14. You have a function handling user authentication, and Copilot generates the following test case:

def test_authenticate_user():  
    assert authenticate("admin", "password123") == True  

What is the most critical issue with this test?
✅ D) All of the above

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?
    ✅ A) Copilot automatically detects API keys and removes them
    ✅ B) Copilot respects repository secrets settings and avoids using secret keys in suggestions

[paulsuv9]

Great job on the quiz answers! ✅ Your approach to ensuring Copilot-generated tests follow a TDD workflow is spot on. Do you have any additional tips for integrating Copilot into a TDD process? 💡

[paulsuv9]

Great insights on using Copilot for generating unit tests! I’ve found it particularly useful for catching edge cases. Have you tried integrating it with your CI/CD pipeline for automated testing?

[RAVINDRA8008]

Great point! Copilot is amazing for edge cases. Automating tests in CI/CD would take it to the next level! Have you set this up? Curious to learn more! 🔍🚀

[paulsuv9]

Yes, I’ve integrated Copilot-generated tests into my CI/CD pipeline using GitHub Actions. It’s been a seamless process and has significantly improved our testing efficiency. I can share a sample workflow file if you’re interested! 🚀

[paulsuv9]

💡Tip: One unique way to use Copilot is to generate security tests. It can help identify potential vulnerabilities early in the development process. The privacy settings are also crucial for protecting sensitive data.

[RAVINDRA8008]

Great point! 🔥 Using Copilot to generate security tests is a game-changer. Catching vulnerabilities early saves so much time and effort! Have you found a specific prompt or approach that works best for this? Would love to hear more! 🚀

[paulsuv9]

Absolutely! One prompt that works well for me is: ‘Generate security tests for input validation and SQL injection.’ This helps Copilot focus on common vulnerabilities. What about you? Any specific prompts that have worked well?

[mvpkenlin]

Here are my answers for the week 4 challenge.

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?

(Choose two correct answers.)
🟢 B) Provide explicit comments describing edge case scenarios before writing the function
🟢 C) Use a combination of property-based testing and manually crafted assertions

Note

A is incorrect. From unit 4 of Develop unit tests using GitHub Copilot tools", we learned that we need to explicit asking GitHub Copilot suggest additional edge cases that should be tested
D is incorrect. Again, we should always fo code review and modify if needed

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

🟢 B) Modify assertions to account for precision tolerance

Note

A is incorrect because we should not ignore the test case
C is incorrect because this is trying to modify the function but there is still chance to have floaing-point precision errors.
D is incorrect because disabling Copilot does not help
So B would be the best answer, by modifying assertions to account for precision tolerance.

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

🟢 A) Write failing test cases before implementing the function

Note

Traditional TDD approach is creating test cases before implmenting the function. if we want to make sure Copilot-generated tests follow the TDD workflow, we should do the same.
B is incorrect as this does not fall into TDD workflow
C is incorrect as this is only updating/modifying the code but it does not fall into TDD workflow too.
D is incorrect as there is no such mode.

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

🟢 B) Providing incomplete function definitions without parameter types

Note

A is incorrect, without internet connection, Copilot cannot work.
C & D are incorrect as they won't lead to incorrect test cases, although that it is not recommended
B is correct becuase your definitions isn't complete.

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?

(Choose two correct answers.)
🟢 A) Asking Copilot Chat to explain unexpected test failures
🟢 C) Providing error logs to Copilot Chat for step-by-step troubleshooting

Note

A is correct, asking Copilot to explain would help us to understand the reason and improve it.
C is correct, with the logs, GitHub Copilot could offer more targeted suggestions.

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

🟢 B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot

Note

A is incorrect, we need to have additional manual effort.
B is correct, we specific ‘test with large JSON data’ in prompt would work
C is incorrect, there is no such setting.
D is incorrect, increasing timeout does not generate large JSON playloads.

7. What is the primary function of GitHub Copilot’s content exclusion settings?

🟢 B) Restrict Copilot from suggesting code similar to private repositories

Note

A is incorrect, GitHub Copilot does not access internal documentation.
B is correct, We could exclude repo so that GitHub Copilot won't use the content to inform its suggestion
C is incorrect, We could not exclude specific types of data
D is incorrect, it doesn't help to limit GitHub Copilot to suggest insecure code patterns
Source: Manage content exclusions - Microsoft Learn

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?

(Choose two correct answers.)
🟢 B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
🟢 D) When Copilot generates suggestions based on similar insecure patterns from public repositories

Note

A is incorrect, it is not cause insecure code.
B is correct, outdated cryptographic is already a secure issue.
C is incorrect, with this settings won't help stopping the chance that GitHub Copilot generate insecure code
D is correct, GitHub Copilot is trained on public repo and it will generate similar insecure code if some public repo contains insecure code.
Resource: Finding existing vulnerabilities in code

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

🟢 B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot

Note

B is correct as we could describe edge cases in the prompt to generate more appropriate test cases.
Resource: Exercise - Create unit tests for specific conditions by using GitHub Copilot

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

🟢 B) Provide comments specifying ‘test async functions’

Note

B is correct, we could explicit instruct GitHub Copilot to generate async test cases like this.

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

🟢 C) Copilot is missing necessary function comments or contextual prompts

Note

A is incorrect, as GitHub Copilot is already trained to write test cases
B is incorrect, it could still able to generate tests
D is incorrect, as long as you have installed the test framework package added to your project, GitHub Copilot should be able to generate test cases.
Resource: Visual Studio Code support for unit tests

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

🟢 C) GitHub Copilot Enterprise

Note

Only Enterprise provides enterprise-wide context exclusion settings.

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

🟢 A) Specify ‘Use secure cryptographic methods’ in comments

Note

A is correct. It is the same as generate test cases for Edge cases. We could explicitly specify it in the prompt when asking GitHub Copilot to generate.

14. You have a function handling user authentication, and Copilot generates the following test case: What is the most critical issue with this test?

🟢 D) All of the above

Note

All A, B, C are correct. This test case does not handle edge cases. It also hardcoded credtial, and there is missing negative test cases.

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?

(Choose two correct answers.)
🟢 B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
🟢 D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials

Note

B & D are correct, GitHub Copilot could only avoid generating credentials
A is incorrect, GitHub Copilot does not able to remove.
C is incorrect, GitHub Copilot does not flag and block, instead you could apply Content Exclusion

[paulsuv9]

Great job on providing detailed answers, @mvpkenlin! 📝 Your explanations are very clear and helpful. I particularly liked your point about providing explicit comments for edge cases to guide Copilot. It’s a practical tip that can significantly improve test coverage. Have you encountered any other interesting challenges while using Copilot for testing? 🚀

[RAVINDRA8008]

Day 3 of the final week, and the momentum is strong! 🚀 Today’s focus: mastering testing, security, and performance with GitHub Copilot. Loving the hands-on learning—so many new ways to improve code quality! 💻🔥 What’s been your biggest takeaway so far? Let’s share and learn together! 💪 #GitHubCopilot #LevelUp #CodeBetter

[paulsuv9]

Your enthusiasm is fantastic! 🔥 Using Copilot for security tests is a smart move. It’s incredible how much more secure our code can be with early vulnerability detection. Have you tried using Copilot to generate tests for specific security scenarios like XSS or CSRF? I’d love to hear your experiences! 🚀

[johnniekpng]

Here are my answers:

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases? (Choose two correct answers.)

B) Provide explicit comments describing edge case scenarios before writing the function: This gives Copilot context, guiding it to generate tests for those specific scenarios.
C) Use a combination of property-based testing and manually crafted assertions: Property-based testing generates many inputs, helping to find edge cases. Manually crafted assertions ensure specific, critical edge cases are tested. Copilot can assist with both.

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

B) Modify assertions to account for precision tolerance: Floating-point arithmetic often leads to slight inaccuracies. Instead of expecting exact equality, use assertions that check if the result is within an acceptable range.

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

A) Write failing test cases before implementing the function: This is the core principle of TDD. Copilot can help you write these initial failing tests based on your descriptions of the intended functionality.

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

B) Providing incomplete function definitions without parameter types: Copilot relies on context. Without type information, it may make incorrect assumptions, leading to flawed tests.

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency? (Choose two correct answers.)

A) Asking Copilot Chat to explain unexpected test failures: Copilot Chat can analyze the code and failure to provide insights.
C) Providing error logs to Copilot Chat for step-by-step troubleshooting: This gives Copilot Chat the necessary context to understand the problem.

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot: Explicitly instructing Copilot about the data size is the most direct way to influence its output.

7. What is the primary function of GitHub Copilot’s content exclusion settings?

B) Restrict Copilot from suggesting code similar to private repositories: Content exclusion prevents Copilot from using or suggesting code from specified files or directories. This is crucial for protecting sensitive code, proprietary algorithms, or internal documentation. It also helps to prevent Copilot from completing code or answering chat prompts.

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled? (Choose two correct answers.)

B) When Copilot generates code for outdated cryptographic practices like MD5 hashing: Copilot's training data might include outdated or insecure practices.
D) When Copilot generates suggestions based on similar insecure patterns from public repositories: This is a fundamental risk. Copilot learns from public code, which may contain vulnerabilities.

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot: Directly guiding Copilot with specific edge case instructions is key.

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

B) Provide comments specifying ‘test async functions’: Clear communication with Copilot through comments is generally the best approach.

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

C) Copilot is missing necessary function comments or contextual prompts: Copilot needs context to generate relevant suggestions. Lack of comments or clear instructions is the most likely cause.

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

C) GitHub Copilot Enterprise: Enterprise offers the most comprehensive features, including organization-wide content exclusion.
Content exclusion is also available for Copilot Business.

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

A) Specify ‘Use secure cryptographic methods’ in comments: Guiding Copilot with specific instructions is the most reliable way to influence its output towards security.

14. You have a function handling user authentication, and Copilot generates the following test case:
def test_authenticate_user():
assert authenticate("admin", "password123") == True
What is the most critical issue with this test?

D) All of the above:

B) It uses hardcoded credentials, which is a security risk: Storing or testing with hardcoded credentials (such as "admin" and "password123") is a bad practice. If this code were exposed or shared, it could reveal sensitive information or give attackers a template for brute-force attacks.
A) The test lacks edge cases: The test only evaluates one specific case: successful authentication with "admin" and "password123". It doesn't test edge cases such as invalid usernames, incorrect passwords, empty inputs, or special characters.
C) Copilot should generate negative test cases as well: A comprehensive test suite for authentication should include negative test cases to ensure the system handles failure scenarios properly.

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code? (Choose two correct answers.)

B) Copilot respects repository secrets settings and avoids using secret keys in suggestions: GitHub Copilot is designed to work with your repository’s secret settings so that any secrets stored there are not inadvertently exposed through AI-generated suggestions.
D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials: This is a key security feature of Copilot.
Copilot has an AI-based vulnerability prevention system.

[mvark]

The wording of Q14 is tricky as it asks for "the most critical issue" but provides an option to select all 3 choices.

[paulsuv9]

Excellent breakdown of the quiz answers! 📝 Your explanation on handling floating-point precision errors was particularly helpful. Do you have any tips on how to handle similar issues in other programming languages? 💡

[mvark]

My answers with reasoning to Week 4 questions -

1. B, C) When leveraging GitHub Copilot for unit testing, the most effective strategies to ensure the generated test cases cover edge cases are:

• Providing explicit comments describing edge case scenarios before writing the function
• Using a combination of property-based testing and manually crafted assertions

2 B) When dealing with floating-point precision errors in test cases suggested by Copilot, the most effective approach is to modify the assertions to account for a precision tolerance. This is because floating-point calculations can often result in small discrepancies due to the inherent imprecision of representing decimal numbers in binary. By allowing for a small margin of error (tolerance) in the assertions, you can make the test more robust and less prone to failing due to minor precision issues.

Python's math.isclose function can be used to compare two floating-point numbers with a specified tolerance.

3 A) To ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow, the best approach is to write failing test cases before implementing the function. This aligns with the core principles of TDD, which emphasizes writing tests before writing the actual code.

While it sounds appealing, there is currently no specific "TDD mode" in Copilot.

4 B) GitHub Copilot may generate non-functional or incorrect test cases if incomplete function definitions without parameter types are provided. This is because Copilot may not be able to infer the correct data types, function behavior, or edge cases.

Copilot doesn't work offline at all, so without an internet connection it won't generate any code rather than incorrect code.

5 A,C)To improve debugging efficiency:

• Ask Copilot Chat to explain unexpected test failures
• Provide error logs to Copilot Chat for step-by-step troubleshooting

6 B) You are working on an API that processes large JSON payloads. If Copilot keeps generating tests with small sample data, provide comments specifying ‘test with large JSON data’ before invoking Copilot. By explicitly mentioning "test with large JSON data" in comments, you signal the intent to generate tests with substantial payloads.

7 B) The primary function of GitHub Copilot’s content exclusion settings is to restrict Copilot from suggesting code similar to private repositories.

When you exclude certain files or directories, GitHub Copilot won't use the content in those files to inform its suggestions. This action can lead to more secure and compliant code suggestions. It's essential to carefully analyze which files should be excluded to balance security and functionality.

8 B, D) Even with security settings enabled, GitHub Copilot may still generate code that uses outdated or insecure cryptographic practices, such as MD5 hashing, if it's trained on codebases that use these practices. This is because Copilot's primary goal is to generate code that is similar to what it has seen before, rather than to ensure the security of the generated code.

Similarly, if Copilot is trained on public repositories that contain insecure code patterns, it may generate suggestions that replicate these patterns, even if security settings are enabled. This is because Copilot's training data is sourced from public repositories, which may not always reflect the latest security best practices.

While Copilot Chat can help find some common security vulnerabilities and help you fix them, you should not rely on Copilot for a comprehensive security analysis. Using security tools and features will more thoroughly ensure your code is secure.

9 B) If Copilot consistently misses testing for integer overflow errors, explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot

By explicitly mentioning integer overflow cases in comments, you're directly guiding Copilot to consider these scenarios. Comments serve as prompts that influence the generated code's focus and coverage.

Copilot doesn't have configuration settings for test generation strictness.

10 B) When writing JavaScript tests with Jest, you can ensure Copilot generates async test cases correctly by providing comments specifying ‘test async functions’.

Comments like "// async test" or "// test async function" signal to Copilot that the test involves asynchronous operations

Jest is an open source project maintained by Facebook, and it's especially well suited for React code testing.

11 C) If Copilot Chat in Visual Studio Code is not suggesting relevant test scenarios, a likely reason is that Copilot is missing necessary function comments or contextual prompts.

GitHub Copilot Chat uses your code's context and semantics to suggest assertions that ensure the function is working correctly.

12 C) GitHub Copilot Enterprise offers enterprise-wide content exclusion settings for security-sensitive codebases.

Organizations and enterprises with a subscription to GitHub Copilot Business or GitHub Copilot Enterprise can prevent Copilot from accessing certain content.

13 B) If you want Copilot to generate secure cryptographic implementations, copying Copilot’s first suggestion and manually verifying security is the best approach among the provided options. It balances Copilot’s assistance with human verification. This ensures that any potential vulnerabilities are identified and addressed before deployment.

While specifying ‘Use secure cryptographic methods’ in comments can guide Copilot, it does not guarantee that the generated code will be secure as the prompt is too generic. Copilot may not fully interpret or implement the security requirements as intended.

There is no explicit "security-enhanced mode" to enable.

As the question explicitly states "You want Copilot to generate secure cryptographic implementations." disabling Copilot when writing encryption functions entirely doesn't seem like a good option.

14 B) The most critical issue with this test case:
def test_authenticate_user():
assert authenticate("admin", "password123") == True
...is that it uses hardcoded credentials, which is a security risk.

The question mentions "most critical issue" so "All of the above" doesn't seem reasonable.

15 B, D) The following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?
Copilot respects repository secrets settings and avoids using secret keys in suggestions
GitHub Copilot’s content filters prevent suggesting hardcoded credentials
While Copilot has preventive measures, it doesn't automatically detect and remove API keys after they're generated. It tries to prevent suggesting them in the first place.

Copilot does more than just flag patterns; it actively tries to prevent suggesting insecure code.

[paulsuv9]

Great job on providing detailed reasoning for each answer, @mvark! 📝 Your explanations are very clear and helpful. I particularly liked your point about using math.isclose for handling floating-point precision errors. It’s a practical tip that can save a lot of headaches. Have you encountered any other interesting challenges while using Copilot for testing? 🚀

[paulsuv9]

This week, we focused on testing, security, and privacy. Here are the key takeaways:

Testing
• Leveraging Copilot for Unit Tests: GitHub Copilot can automatically generate unit tests, which helps in ensuring that your code behaves as expected. By providing clear comments and context, Copilot can create comprehensive test cases that cover various scenarios, including edge cases.
o Example: “Generate unit tests for input validation and boundary conditions.”
o Benefit: Saves time and improves test coverage, leading to more robust code.

Security
• Enhancing Code Quality: Copilot assists in identifying and fixing potential security vulnerabilities early in the development process. It can suggest secure coding practices and generate security tests to catch issues like SQL injection, XSS, and CSRF.
o Example: “Generate security tests for SQL injection and XSS vulnerabilities.”
o Benefit: Reduces the risk of security breaches and ensures compliance with security standards.

Privacy
• Configuring Content Exclusions: To protect sensitive information, Copilot allows you to configure content exclusions. This ensures that private or proprietary code is not used in Copilot’s suggestions, maintaining privacy and data protection.
o Example: “Exclude sensitive data and proprietary code from Copilot suggestions.”
o Benefit: Enhances privacy and compliance with data protection regulations.

Practical Applications
• CI/CD Integration: Integrating Copilot-generated tests into your CI/CD pipeline can automate the testing process, ensuring that new code changes do not introduce bugs or vulnerabilities.
o Example: “Use GitHub Actions to run Copilot-generated tests on every pull request.”
o Benefit: Streamlines the development workflow and maintains code quality.

Community Insights
• Sharing and Learning: Engaging with the community by sharing your experiences and learning from others can provide new perspectives and tips on using Copilot effectively.
o Example: “Participate in discussions and share your unique use cases and prompts.”
o Benefit: Fosters a collaborative learning environment and enhances your understanding of Copilot’s capabilities.

[paulsuv9]

💡Tip: Use Copilot to create privacy-focused scripts that anonymize user data before processing. It’s a great addition to data handling practices!

[paulsuv9]

Here are my answers to the Week 4 quiz:

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases? (Choose two correct answers.)
   o B) Provide explicit comments describing edge case scenarios before writing the function
    Pro Tip: Always describe edge cases in comments to guide Copilot in generating more comprehensive tests. 📝
   o C) Use a combination of property-based testing and manually crafted assertions
    Pro Tip: Combining different testing strategies ensures broader coverage and more robust tests. 🔍

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?
   o B) Modify assertions to account for precision tolerance
    Pro Tip: Use functions like math.isclose in Python to handle floating-point precision issues. 🔧

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?
   o A) Write failing test cases before implementing the function
    Pro Tip: Writing tests first helps you think through the requirements and edge cases before coding. 🧠

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?
   o B) Providing incomplete function definitions without parameter types
    Pro Tip: Always provide complete function definitions with parameter types to help Copilot generate accurate tests. 🛠️

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency? (Choose two correct answers.)
   o A) Asking Copilot Chat to explain unexpected test failures
    Pro Tip: Use Copilot Chat to get insights into why a test failed and how to fix it. 💬
   o C) Providing error logs to Copilot Chat for step-by-step troubleshooting
    Pro Tip: Detailed error logs help Copilot Chat provide more accurate troubleshooting steps. 📝

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?
   o B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
    Pro Tip: Explicitly instructing Copilot with comments ensures it generates tests that match your requirements. 📋

7. What is the primary function of GitHub Copilot’s content exclusion settings?
   o B) Restrict Copilot from suggesting code similar to private repositories
    Pro Tip: Use content exclusion settings to protect sensitive code and ensure compliance. 🔒

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled? (Choose two correct answers.)
   o B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
    Pro Tip: Always review and update cryptographic practices to ensure security. 🔐
   o D) When Copilot generates suggestions based on similar insecure patterns from public repositories
    Pro Tip: Regularly audit and review Copilot’s suggestions for security vulnerabilities. 🛡️

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?
   o B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
    Pro Tip: Clear and detailed comments help Copilot generate more comprehensive tests. 📝

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?
    o B) Provide comments specifying ‘test async functions’
     Pro Tip: Use comments to guide Copilot in generating accurate async test cases. ⏳

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?
    o C) Copilot is missing necessary function comments or contextual prompts
     Pro Tip: Provide detailed comments and context to help Copilot generate relevant test scenarios. 📝

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?
    o C) GitHub Copilot Enterprise
     Pro Tip: Use enterprise-wide settings to manage security and compliance across your organization. 🏢

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?
    o A) Specify ‘Use secure cryptographic methods’ in comments
     Pro Tip: Clear instructions in comments help Copilot generate secure code. 🔐

14. You have a function handling user authentication, and Copilot generates the following test case:
    def test_authenticate_user():
    assert authenticate("admin", "password123") == True

What is the most critical issue with this test?
o D) All of the above
 Pro Tip: Avoid hardcoded credentials and ensure tests cover both positive and negative cases. 🔒

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code? (Choose two correct answers.)
    o B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
     Pro Tip: Use repository secrets settings to protect sensitive information. 🔑
    o D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials
     Pro Tip: Regularly review and update content filters to enhance security.

[Ronaldo-001]

Great Answers @paulsuv9

[kareem1207]

Sorry for being late to respond—thanks for sharing these amazing resources, @Akash1134! Here are my answers, and I hope they’re correct:

1. When leveraging GitHub Copilot for unit testing, which of the following are the most effective strategies to ensure the generated test cases cover edge cases? (Select two)
   A) Rely on Copilot’s first test suggestion and refine manually.
   B) Provide explicit comments describing edge case scenarios before writing the function. ✅
   C) Use a combination of property-based testing and manually crafted assertions. ✅
   D) Trust Copilot’s completion and execute tests without modifications.

2. Copilot suggests a test case that fails due to floating-point precision errors. What is the best course of action?
   A) Ignore the test case and generate a new one.
   B) Modify assertions to account for precision tolerance. ✅
   C) Use Copilot Chat to rewrite the function with better numerical stability.
   D) Disable Copilot when dealing with floating-point calculations.

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?
   A) Write failing test cases before implementing the function. ✅
   B) Let Copilot generate tests after writing the function.
   C) Use Copilot Chat to refactor existing tests post-development.
   D) Enable Copilot’s TDD mode.

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?
   A) Using Copilot without an internet connection.
   B) Providing incomplete function definitions without parameter types. ✅
   C) Using Copilot with multiple test frameworks in the same file.
   D) Asking Copilot to generate tests for functions written in a different programming language.

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency? (Select two)
   A) Asking Copilot Chat to explain unexpected test failures. ✅
   B) Using Copilot Chat to automatically resolve compilation errors.
   C) Providing error logs to Copilot Chat for step-by-step troubleshooting. ✅
   D) Relying entirely on Copilot’s debugging suggestions without verification.

6. You are working on an API that processes large JSON payloads, but Copilot keeps generating tests with small sample data. How can you address this?
   A) Manually modify Copilot’s output to include large payloads.
   B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot. ✅
   C) Enable Copilot’s ‘large dataset’ mode in settings.
   D) Increase the timeout value for Copilot to process larger requests.

7. What is the primary function of GitHub Copilot’s content exclusion settings?
   A) Prevent Copilot from accessing sensitive internal documentation.
   B) Restrict Copilot from suggesting code similar to private repositories. ✅
   C) Block Copilot from using specific types of data when generating suggestions.
   D) Limit Copilot’s ability to suggest insecure code patterns.

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?
   A) When Copilot is used without context-aware suggestions. ✅
   B) When Copilot generates code for outdated cryptographic practices like MD5 hashing.
   C) When Copilot is used in a private repository with strict security settings.
   D) When Copilot generates suggestions based on similar insecure patterns from public repositories.

9. You are using GitHub Copilot to generate tests for a function handling financial transactions, but it consistently misses testing for integer overflow errors. What should you do?
   A) Increase the number of test cases Copilot generates.
   B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot. ✅
   C) Use Copilot Chat to ask for ‘comprehensive financial validation tests’.
   D) Modify Copilot’s configuration to generate stricter tests.

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?
    A) Add @async as an annotation.
    B) Provide comments specifying ‘test async functions’. ✅
    C) Manually modify Copilot-generated test cases.
    D) Change Jest’s settings to enable async mode.

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot suggests irrelevant test scenarios. What might be the reason?
    A) Copilot is not trained to write test cases.
    B) Copilot only generates tests when a function is explicitly documented.
    C) Copilot is missing necessary function comments or contextual prompts. ✅
    D) The test framework is not supported by Copilot.

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?
    A) GitHub Copilot Individual.
    B) GitHub Copilot for Business.
    C) GitHub Copilot Enterprise. ✅
    D) GitHub Copilot Pro.

13. You want Copilot to generate secure cryptographic implementations. What is the best approach?
    A) Specify ‘Use secure cryptographic methods’ in comments. ✅
    B) Copy Copilot’s first suggestion and manually verify security.
    C) Disable Copilot when writing encryption functions.
    D) Enable Copilot’s security-enhanced mode.

14. You have a function handling user authentication, and Copilot generates the following test case:

    def test_authenticate_user():
        assert authenticate("admin", "password123") == True

    What is the most critical issue with this test?
    A) The test lacks edge cases.
    B) It uses hardcoded credentials, which is a security risk.
    C) Copilot should generate negative test cases as well.
    D) All of the above. ✅

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code? (Select two)
    A) Copilot automatically detects API keys and removes them. ✅
    B) Copilot respects repository secrets settings and avoids using secret keys in suggestions. ✅
    C) Copilot flags insecure patterns but does not block suggestions.
    D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials.

This week was a real challenge but also incredibly engaging!

[Akash1134]

That’s a Wrap – and a Big Thank You!

We’ve reached the end of Week 4 of the Copilot Free Learning & Certification Prep, and what an incredible journey it’s been! Your dedication, curiosity, and engagement have made this experience truly special.

🏆 And the Winners Are… (Almost!)

The top participants will be announced on or before March 18th—so stay tuned! Certification vouchers will be sent out shortly after, so keep an eye on your inbox.

🔎 How will you select the winners?

We’re all about fairness and transparency. Winners will be shortlisted based on:

• ✔️ Consistent engagement and thoughtful contributions
• ✔️ Participation in surveys & challenges
• ✔️ Detailed, accurate responses to practice questions
• ✔️ Demonstrated effort in applying resources before attempting exercises

This learning experience wouldn’t have been the same without you! Thank you for showing up, sharing your insights, and pushing yourself to grow. We can’t wait to celebrate our winners soon—but no matter the outcome, you’ve already won by investing in your skills! 🚀

Finally, Here are the correct answers to the practice questions of Week 4:

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?

(Choose two correct answers.)

A) Rely on Copilot’s first test suggestion and refine manually
B) Provide explicit comments describing edge case scenarios before writing the function
C) Use a combination of property-based testing and manually crafted assertions
D) Trust Copilot’s completion and execute tests without modifications

Correct Answers: B, C
Correct Answers and Reasoning: Providing explicit comments ensures that Copilot understands edge case requirements. Property-based testing helps verify a wider range of inputs, complementing Copilot’s generated cases.

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

A) Ignore the test case and generate a new one
B) Modify assertions to account for precision tolerance
C) Use Copilot Chat to rewrite the function with better numerical stability
D) Disable Copilot when dealing with floating-point calculations

Correct Answer: B
Correct Answers and Reasoning: Floating-point calculations can introduce precision errors. Adjusting assertions with a tolerance (e.g., using assertAlmostEqual) ensures reliable comparisons.

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

A) Write failing test cases before implementing the function
B) Let Copilot generate tests after writing the function
C) Use Copilot Chat to refactor existing tests post-development
D) Enable Copilot’s TDD mode

Correct Answer: A
Correct Answers and Reasoning: TDD requires writing failing tests first to guide function development. Copilot can assist by generating tests, but they should be written before implementation.

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

A) Using Copilot without an internet connection
B) Providing incomplete function definitions without parameter types
C) Using Copilot with multiple test frameworks in the same file
D) Asking Copilot to generate tests for functions written in a different programming language

Correct Answers: B, C
Correct Answers and Reasoning: Copilot relies on contextual function details, and missing parameter types reduce accuracy. Multiple test frameworks can confuse Copilot, leading to inconsistent suggestions.

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?

(Choose two correct answers.)

A) Asking Copilot Chat to explain unexpected test failures
B) Using Copilot Chat to automatically resolve compilation errors
C) Providing error logs to Copilot Chat for step-by-step troubleshooting
D) Relying entirely on Copilot’s debugging suggestions without verification

Correct Answers: A, C
Correct Answers and Reasoning: Copilot Chat can analyze errors and provide explanations, but human validation is essential. Providing error logs improves Copilot’s debugging assistance.

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

A) Manually modify Copilot’s output to include large payloads
B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
C) Enable Copilot’s ‘large dataset’ mode in settings
D) Increase the timeout value for Copilot to process larger requests

Correct Answer: B
Correct Answers and Reasoning: Adding a comment instructing Copilot to generate tests with large payloads helps it better understand requirements.

7. What is the primary function of GitHub Copilot’s content exclusion settings?

A) Prevent Copilot from accessing sensitive internal documentation
B) Restrict Copilot from suggesting code similar to private repositories
C) Block Copilot from using specific types of data when generating suggestions
D) Limit Copilot’s ability to suggest insecure code patterns

Correct Answer: B
Correct Answers and Reasoning: Content exclusion settings help ensure Copilot doesn’t suggest code derived from private repositories, enhancing security.

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?

(Choose two correct answers.)

A) When Copilot is used without context-aware suggestions
B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
C) When Copilot is used in a private repository with strict security settings
D) When Copilot generates suggestions based on similar insecure patterns from public repositories

Correct Answers: B, D
Correct Answers and Reasoning: Copilot may generate insecure code if it follows outdated cryptographic practices or patterns from publicly available but insecure sources.

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

A) Increase the number of test cases Copilot generates
B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
C) Use Copilot Chat to ask for ‘comprehensive financial validation tests’
D) Modify Copilot’s configuration to generate stricter tests

Correct Answer: B
Correct Answers and Reasoning: Adding comments specifying edge cases, such as integer overflow, helps Copilot generate more targeted test cases.

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

A) Add @async as an annotation
B) Provide comments specifying ‘test async functions’
C) Manually modify Copilot-generated test cases
D) Change Jest’s settings to enable async mode

Correct Answer: B
Correct Answers and Reasoning: Explicitly instructing Copilot to generate async tests ensures correct handling of asynchronous behavior in Jest.

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

A) Copilot is not trained to write test cases
B) Copilot only generates tests when a function is explicitly documented
C) Copilot is missing necessary function comments or contextual prompts
D) The test framework is not supported by Copilot

Correct Answer: C
Correct Answers and Reasoning: Copilot relies on function comments and context to generate relevant test cases. Lack of these details affects its accuracy.

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

A) GitHub Copilot Individual
B) GitHub Copilot for Business
C) GitHub Copilot Enterprise
D) GitHub Copilot Pro

Correct Answer: C
Correct Answers and Reasoning: GitHub Copilot Enterprise includes advanced security and content exclusion settings for enterprise-wide enforcement.

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

A) Specify ‘Use secure cryptographic methods’ in comments
B) Copy Copilot’s first suggestion and manually verify security
C) Disable Copilot when writing encryption functions
D) Enable Copilot’s security-enhanced mode

Correct Answer: A
Correct Answers and Reasoning: Providing explicit instructions helps Copilot generate secure cryptographic implementations.

### 14. You have a function handling user authentication, and Copilot generates the following test case:
```python
def test_authenticate_user():
    assert authenticate("admin", "password123") == True

What is the most critical issue with this test?

A) The test lacks edge cases
B) It uses hardcoded credentials, which is a security risk
C) Copilot should generate negative test cases as well
D) All of the above

Answer: D

Reasoning: The test lacks edge cases, uses hardcoded credentials (a security risk), and does not include negative test cases, making D) All of the above the most comprehensive answer.

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?

(Choose two correct answers.)

A) Copilot automatically detects API keys and removes them
B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
C) Copilot flags insecure patterns but does not block suggestions
D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials

Answers: B, D

Reasoning: Copilot prevents credential leaks by respecting repository secret settings and blocking suggestions of hardcoded credentials, making B and D the correct choices.

[Ronaldo-001]

So Excited !! @Akash1134

[mvark]

It's interesting to see that questions in the series like Q4 this week have multiple answers even though the question didn't explicitly ask for it.

I feel the answer to Q13 about which approach for Copilot to generate secure cryptographic implementations is the best, is subjective. I had a tough time picking between options A & B as both are not perfect.

My objection to option "A) Specify ‘Use secure cryptographic methods’ in comments" is that it was too generic. Would simply asking for "secure" methods without any context-specific details guarantee security? The documentation strongly emphasizes the importance of manual review and testing of generated code, especially for security-critical applications.

My choice option "B) Copy Copilot’s first suggestion and manually verify security" is weak only if we assume the developer is not a security expert.

For Q14, I was fixated on the grammar. As the question asks for "most critical issue" not "issues" I choose B as I felt it was the most significant security risk among the others.

[VCharrua]

Hello @mvark ... for Q14, the term "most critical issue" introduced some doubt in obtaining the answer (which I had not even properly valued previously 😊). In the Writing tests with GitHub Copilot guide, we can easily see that all options should be taken into account, however, if we consider which would be the most critical, perhaps answer B) would certainly be one of the most accurate.

For Q13, I don't really see option B) as a possible answer since it could introduce some risk.

[mvark]

Considering the following additional points from the documentation on Responsible use of GitHub Copilot, I still feel that the mention of "manually verify security" in option B makes it a better choice -

"Copilot Chat has been trained on a large body of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages."

"Code generated by Copilot may contain security vulnerabilities or other issues. You should always carefully review and test code generated by Copilot."

[VCharrua]

@mvark, yes.. I agree, we should always carefully review and test code generated by Copilot... but that applies intrinsically to both options. From what we learned last week, the specificity of the prompt will help Copilot to suggest better code, and in this case, more secure cryptographic implementations. It will be interesting to see the @Akash1134 response and reasoning to that question. 😊

[pintuyadav5468]

Here are my answers to the Week 4 quiz:

1. Most effective strategy to ensure edge cases are covered:
   B) Provide explicit comments describing edge case scenarios before writing the function
   C) Use a combination of property-based testing and manually crafted assertions

2. Handling floating-point precision errors:
   B) Modify assertions to account for precision tolerance

3. Ensuring TDD workflow with Copilot:
   A) Write failing test cases before implementing the function

4. Scenarios causing incorrect test cases:
   B) Providing incomplete function definitions without parameter types
   C) Using Copilot with multiple test frameworks in the same file

5. Strategies for debugging with Copilot Chat:
   A) Asking Copilot Chat to explain unexpected test failures
   C) Providing error logs to Copilot Chat for step-by-step troubleshooting

6. Fixing Copilot generating small JSON payloads in tests:
   A) Manually modify Copilot’s output to include large payloads
   B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot

7. Purpose of Copilot’s content exclusion settings:
   B) Restrict Copilot from suggesting code similar to private repositories

8. Situations where Copilot might generate insecure code:
   A) When Copilot is used without context-aware suggestions
   B) When Copilot generates code for outdated cryptographic practices like MD5 hashing

9. Handling missing integer overflow tests in financial transactions:
   B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot

10. Ensuring Copilot generates async test cases correctly in Jest:
    B) Provide comments specifying ‘test async functions’

11. Why Copilot is not suggesting relevant test scenarios:
    C) Copilot is missing necessary function comments or contextual prompts

12. GitHub Copilot SKU offering enterprise-wide security settings:
    C) GitHub Copilot Enterprise

13. Generating secure cryptographic implementations:
    A) Specify ‘Use secure cryptographic methods’ in comments

14. Most critical issue with the test case:
    D) All of the above

15. Copilot security safeguards for credential exposure:
    B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
    D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials

[VCharrua]

This is my initial contribution for Week 4. Thanks to @Akash1134 for these amazing resources (as always)! ☺️

Knowledge Checkpoint Answers

1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?

(Choose two correct answers.)

• A) Rely on Copilot’s first test suggestion and refine manually
• B) Provide explicit comments describing edge case scenarios before writing the function
• C) Use a combination of property-based testing and manually crafted assertions
• D) Trust Copilot’s completion and execute tests without modifications

Explanation:

GitHub Copilot generates test cases based on the context it's given. By providing explicit comments describing edge cases before writing the function (option B), you guide Copilot to consider these scenarios when generating tests. This approach is recommended in the documentation on writing tests with GitHub Copilot, which suggests that providing clear comments improves the quality of generated tests.

Additionally, combining property-based testing with manually crafted assertions (option C) enhances test coverage. Property-based testing allows for systematic exploration of edge cases, while manual assertions ensure specific edge cases are explicitly tested.

Sources:

• Writing tests with GitHub Copilot
• How to write better prompts for GitHub Copilot

---

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

• A) Ignore the test case and generate a new one
• B) Modify assertions to account for precision tolerance
• C) Use Copilot Chat to rewrite the function with better numerical stability
• D) Disable Copilot when dealing with floating-point calculations

Explanation:

When dealing with floating-point calculations, precision errors are common. The correct approach is to modify assertions to account for precision tolerance (option B), rather than expecting exact equality. This might involve using assertions that check if values are within an acceptable range or using specialized testing functions for floating-point comparisons.

Option A (ignoring the test) fails to address the underlying issue and would miss potential bugs. Option C (rewriting the function) may be excessive when the issue is just with test expectations, not the function itself. Option D (disabling Copilot) is unnecessary and counterproductive.

Sources:

• Testing code
• Develop unit tests using GitHub Copilot tools

---

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TD- [ ] D) workflow. What is the best approach?

• A) Write failing test cases before implementing the function
• B) Let Copilot generate tests after writing the function
• C) Use Copilot Chat to refactor existing tests post-development
• D) Enable Copilot’s TDD mode

Explanation:

Test-Driven Development (TDD) follows a "Red-Green-Refactor" cycle where you first write failing tests, then implement the code to pass those tests, and finally refactor the code. The best approach to follow TDD with Copilot is to write failing test cases before implementing the function (option A).

This aligns with TDD principles and allows Copilot to understand the expected functionality before generating the implementation. Options B and C contradict TDD methodology by writing tests after implementation. Option D is incorrect because there is no specific "TDD mode" in Copilot.

Sources:

• Develop unit tests using GitHub Copilot tools
• Writing tests with GitHub Copilot

---

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

• A) Using Copilot without an internet connection
• B) Providing incomplete function definitions without parameter types
• C) Using Copilot with multiple test frameworks in the same file
• D) Asking Copilot to generate tests for functions written in a different programming language

Explanation:

GitHub Copilot relies on context to generate accurate code suggestions. Providing incomplete function definitions without parameter types (option B) reduces the quality of Copilot's suggestions because it lacks critical information about what the function expects and returns.

Option A is incorrect because Copilot can work offline with cached suggestions. Option C is unlikely to cause issues as Copilot can usually understand multiple test frameworks. For option D, Copilot can generate tests across different languages, though they may require adjustments.

Sources:

• How GitHub Copilot is getting better at understanding your code
• How to write better prompts for GitHub Copilot

---

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?

(Choose two correct answers.)

• A) Asking Copilot Chat to explain unexpected test failures
• B) Using Copilot Chat to automatically resolve compilation errors
• C) Providing error logs to Copilot Chat for step-by-step troubleshooting
• D) Relying entirely on Copilot’s debugging suggestions without verification

Explanation:

GitHub Copilot Chat enhances debugging by providing contextual assistance. Asking Copilot Chat to explain unexpected test failures (option A) leverages its ability to analyze code and identify potential issues. Similarly, providing error logs for step-by-step troubleshooting (option C) gives Copilot Chat the context needed to suggest targeted solutions.

Option B suggests automatic resolution of compilation errors, which could lead to inappropriate fixes without human oversight. Option D (relying entirely on Copilot's suggestions without verification) is not recommended as developers should always verify AI-generated solutions.

Sources:

• Introducing GitHub Copilot Agent Mode
• Implement code improvements using GitHub Copilot tools

---

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

• A) Manually modify Copilot’s output to include large payloads
• B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
• C) Enable Copilot’s ‘large dataset’ mode in settings
• D) Increase the timeout value for Copilot to process larger requests

Explanation:

To guide Copilot in generating more appropriate tests, providing explicit comments (option B) is the most effective approach. By specifying "test with large JSON data" before invoking Copilot, you direct it to generate tests with larger payloads.

Option A involves manual work after Copilot generates suggestions, which is less efficient. Options C and D are incorrect as there is no "large dataset mode" or timeout setting that affects data size in Copilot's suggestions.

Sources:

• How to write better prompts for GitHub Copilot
• Writing tests with GitHub Copilot
• Testing code
• Develop unit tests using GitHub Copilot tools

---

7. What is the primary function of GitHub Copilot’s content exclusion settings?

• A) Prevent Copilot from accessing sensitive internal documentation
• B) Restrict Copilot from suggesting code similar to private repositories
• C) Block Copilot from using specific types of data when generating suggestions
• D) Limit Copilot’s ability to suggest insecure code patterns

Explanation:

GitHub Copilot's content exclusion settings primarily function to block Copilot from using specific types of data when generating suggestions (option C). This feature allows organizations to prevent Copilot from using certain patterns, code snippets, or data types that might be sensitive or proprietary.

While option B touches on a related concept, the content exclusion feature is broader than just private repositories. Options A and D describe security features but not specifically the content exclusion functionality.

Sources:

• Configuring and auditing content exclusion
• Management and customization considerations with GitHub Copilot

---

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?

(Choose two correct answers.)

• A) When Copilot is used without context-aware suggestions
• B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
• C) When Copilot is used in a private repository with strict security settings
• D) When Copilot generates suggestions based on similar insecure patterns from public repositories

Explanation:

GitHub Copilot may generate insecure code when it suggests outdated cryptographic practices like MD5 hashing (option B), which remain common in code bases despite being insecure. Similarly, if insecure patterns exist in public repositories, Copilot might suggest similar patterns (option D), as it learns from public code.

Option A is incorrect because context-aware suggestions should improve security, not decrease it. Option C is incorrect because private repositories with strict security settings should reduce, not increase, insecure suggestions.

Sources:

• Finding existing vulnerabilities in code
• GitHub Copilot Trust Center

---

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

• A) Increase the number of test cases Copilot generates
• B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
• C) Use Copilot Chat to ask for ‘comprehensive financial validation tests’
• D) Modify Copilot’s configuration to generate stricter tests

Explanation:

The most effective approach is to explicitly add comments describing the edge cases you want to test (option B). By specifically mentioning integer overflows before invoking Copilot, you provide critical context that helps it generate more comprehensive tests.

Option A (increasing test cases) doesn't address the specific gap. Option C is too vague, as "comprehensive financial validation tests" doesn't explicitly mention integer overflows. Option D is incorrect as there's no specific configuration for stricter tests.

Sources:

• How to write better prompts for GitHub Copilot
• Writing tests with GitHub Copilot

---

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

• A) Add @async as an annotation
• B) Provide comments specifying ‘test async functions’
• C) Manually modify Copilot-generated test cases
• D) Change Jest’s settings to enable async mode

Explanation:

To guide Copilot in generating appropriate async test cases with Jest, providing clear comments (option B) is most effective. By specifying "test async functions," you explicitly tell Copilot that asynchronous testing is needed.

Option A is incorrect as @async is not a standard annotation used by Copilot. Option C involves manual work after generation. Option D is incorrect because Jest doesn't have a specific "async mode" setting.

Sources:

• Writing tests with GitHub Copilot
• How to write better prompts for GitHub Copilot

---

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

• A) Copilot is not trained to write test cases
• B) Copilot only generates tests when a function is explicitly documented
• C) Copilot is missing necessary function comments or contextual prompts
• D) The test framework is not supported by Copilot

Explanation:

GitHub Copilot generates more relevant suggestions when it has sufficient context. If Copilot isn't suggesting relevant test scenarios, it's likely missing necessary function comments or contextual prompts (option C). Without clear context about the function's purpose and expected behavior, Copilot cannot generate appropriately targeted tests.

Option A is incorrect because Copilot is definitely trained to write test cases. Option B is incorrect as Copilot can generate tests even without explicit documentation. Option D is incorrect because Copilot supports most major testing frameworks.

Sources:

• How GitHub Copilot is getting better at understanding your code
• How to write better prompts for GitHub Copilot

---

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

• A) GitHub Copilot Individual
• B) GitHub Copilot for Business
• C) GitHub Copilot Enterprise
• D) GitHub Copilot Pro

Explanation:

GitHub Copilot Enterprise offers the most comprehensive enterprise-wide content exclusion settings for security-sensitive codebases. It provides organization-level policy controls, including advanced content exclusion settings that help enterprises protect their intellectual property and sensitive code.

According to GitHub's documentation on subscription plans, Copilot Enterprise includes features specifically designed for enterprise security and compliance needs, including more advanced content exclusion capabilities than those available in Copilot for Business or the individual plans.

Sources:

• Subscription plans for GitHub Copilot (SKUs)
• Configuring and auditing content exclusion

---

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

• A) Specify ‘Use secure cryptographic methods’ in comments
• B) Copy Copilot’s first suggestion and manually verify security
• C) Disable Copilot when writing encryption functions
• D) Enable Copilot’s security-enhanced mode

Explanation:

When working with security-sensitive code like cryptographic implementations, providing explicit guidance to Copilot is the best approach. By specifying "Use secure cryptographic methods" in comments (option A), you direct Copilot to prioritize security and modern best practices in its suggestions.

Option B (copy and manually verify) introduces risk as verification may miss issues. Option C (disabling Copilot) eliminates a useful tool. Option D is incorrect as there is no specific "security-enhanced mode" in Copilot.

Sources:

• How to write better prompts for GitHub Copilot
• Finding existing vulnerabilities in code

---

14. You have a function handling user authentication, and Copilot generates the following test case:

def test_authenticate_user():
    assert authenticate("admin", "password123") == True

What is the most critical issue with this test?

• A) The test lacks edge cases
• B) It uses hardcoded credentials, which is a security risk
• C) Copilot should generate negative test cases as well
• D) All of the above

Explanation:

The test case has multiple critical issues, making "All of the above" (option D) the correct answer:

A) The test lacks edge cases (like invalid credentials, empty strings, SQL injection attempts)
B) It uses hardcoded credentials, which is a security risk (credentials should never be in code)
C) Copilot should generate negative test cases (testing authentication failures)

These issues collectively make the test inadequate and potentially insecure, requiring significant improvements before it could be considered production-ready.

Sources:

• Testing code
• Writing tests with GitHub Copilot

---

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?

(Choose two correct answers.)

• A) Copilot automatically detects API keys and removes them
• B) Copilot respects repository secrets settings and avoids using secret keys in suggestions
• C) Copilot flags insecure patterns but does not block suggestions
• D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials

Explanation:

GitHub Copilot includes multiple security safeguards to protect credentials:

Option B is correct because Copilot is designed to respect repository secrets settings and avoid using secret keys in its suggestions, helping prevent accidental credential exposure.

Option D is also correct because Copilot's content filters are specifically designed to prevent suggesting hardcoded credentials, which is a common security vulnerability.

Option A is incorrect because while Copilot tries to avoid suggesting API keys, it doesn't automatically detect and remove them with perfect accuracy. Option C is partially correct but incomplete, as Copilot's security features aim to prevent suggesting insecure patterns, not just flag them.

Sources:

• GitHub Copilot Trust Center

• How to responsibly adopt GitHub Copilot with the GitHub Copilot Trust Center

• Manage GitHub Copilot

• About GitHub Advanced Security

• About secret scanning

---

Will include my take on the subject matter in the response below... 🚀🚀🚀

[VCharrua]

Summary of the Importance of Testing, Security & Privacy with GitHub Copilot

This week subject matter focusing on Testing, Security & Privacy with GitHub Copilot is critically important for several key reasons:

Enhanced Code Quality Through Testing

Leveraging GitHub Copilot for testing transforms the development process by automating test generation, which significantly improves code quality. Copilot can generate comprehensive test cases covering edge cases that developers might miss, leading to more robust software. By providing explicit guidance through comments, developers can ensure Copilot creates tests that address specific concerns and scenarios, further enhancing software reliability.

Security and Performance Optimization

GitHub Copilot helps identify and prevent security vulnerabilities in code by suggesting secure coding patterns and flagging potential risks. When properly guided, Copilot can generate code that follows security best practices and avoids common pitfalls like hardcoded credentials or vulnerable cryptographic implementations. This proactive approach to security strengthens applications against potential threats while also optimizing performance.

Privacy Protection and Content Exclusion

Understanding content exclusion capabilities allows organizations to protect sensitive intellectual property and prevent Copilot from using certain types of data in its suggestions. Different GitHub Copilot subscription plans (SKUs) offer varying levels of content exclusion features, with enterprise options providing more granular control. This knowledge is crucial for companies handling proprietary code or sensitive information.

Effective Safeguards and Troubleshooting

Knowing how to implement safeguards and troubleshoot issues with GitHub Copilot ensures organizations can responsibly adopt AI-assisted coding while maintaining control over code quality and security. These skills help establish appropriate guardrails for Copilot usage, minimizing risks while maximizing productivity benefits.

In summary, mastering these aspects of GitHub Copilot usage enables developers to write more reliable, secure code while protecting sensitive information - ultimately leading to higher quality software products delivered with greater efficiency.

And happy studies to all! 😄

[VCharrua]

@hemanthnutakki hello... of course...
I think the objective is to have a discussion that will allow us to collectively improve our knowledge about this solution, so feel free to add any comment or insight. 😊

I'm still finishing up a repo with a project that I'm building exclusively with GitHub Copilot, so I'm still coming back with a few more comments to follow up. 🚀🚀

[GustavoBC2007]

Hi everyone! I hope you are doing well, here are my answers for this final week:
1. When leveraging GitHub Copilot for unit testing, what is the most effective strategy to ensure the generated test cases cover edge cases?
(Choose two correct answers.)

✅ B) Provide explicit comments describing edge case scenarios before writing the function
❓ Adding detailed comments helps Copilot understand the specific cases that need testing, improving its suggestions.

✅ C) Use a combination of property-based testing and manually crafted assertions
❓ Property-based testing ensures broad test coverage by checking properties over a range of inputs, complementing Copilot’s test suggestions.

2. Copilot suggests a test case that fails due to floating-point precision errors. What should you do?

✅ B) Modify assertions to account for precision tolerance
❓ Floating-point calculations can introduce minor inaccuracies. Using assertAlmostEqual or setting a tolerance prevents false failures.

3. You need to ensure that Copilot-generated tests follow a Test-Driven Development (TDD) workflow. What is the best approach?

✅ A) Write failing test cases before implementing the function
❓ TDD emphasizes writing tests first, ensuring that development is driven by clear specifications and expected behaviors.

4. Which of these scenarios could cause GitHub Copilot to generate non-functional or incorrect test cases?

✅ B) Providing incomplete function definitions without parameter types
❓ Copilot relies on function signatures for context. Missing parameter types or descriptions may result in inaccurate test cases.

5. GitHub Copilot Chat provides enhanced debugging capabilities. Which of the following strategies improve debugging efficiency?
(Choose two correct answers.)

✅ A) Asking Copilot Chat to explain unexpected test failures
❓ Copilot Chat can analyze error messages and suggest possible causes, helping developers debug more effectively.

✅ C) Providing error logs to Copilot Chat for step-by-step troubleshooting
❓ Supplying error logs gives Copilot more context, enabling it to provide better recommendations for debugging.

6. You are working on an API that processes large JSON payloads. Copilot keeps generating tests with small sample data. How do you fix this?

✅ B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot
❓ Explicit instructions in comments help Copilot generate test cases that align with the required data sizes.

7. What is the primary function of GitHub Copilot’s content exclusion settings?

✅ B) Restrict Copilot from suggesting code similar to private repositories
❓ Content exclusion settings help prevent Copilot from generating suggestions that could resemble proprietary or sensitive code.

8. In what situation might GitHub Copilot generate insecure code, even when security settings are enabled?
(Choose two correct answers.)

✅ A) When Copilot is used without context-aware suggestions
❓ Without specific context, Copilot may generate generic code that does not adhere to security best practices.

✅ B) When Copilot generates code for outdated cryptographic practices like MD5 hashing
❓ Copilot might suggest insecure algorithms if not explicitly guided toward modern cryptographic standards.

9. You are using GitHub Copilot to generate tests for a function handling financial transactions. However, Copilot consistently misses testing for integer overflow errors. What should you do?

✅ B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot
❓ Specifying such edge cases in comments helps Copilot generate more comprehensive test cases.

10. When writing JavaScript tests with Jest, how can you ensure Copilot generates async test cases correctly?

✅ B) Provide comments specifying ‘test async functions
❓ Clearly stating the need for asynchronous test handling ensures Copilot generates the correct structure.

11. A developer is generating test cases using Copilot Chat in Visual Studio Code but notices that Copilot is not suggesting relevant test scenarios. What could be the reason?

✅ C) Copilot is missing necessary function comments or contextual prompts
❓ Without sufficient context, Copilot may not generate accurate or relevant test cases.

12. Which of the following GitHub Copilot SKUs offers enterprise-wide content exclusion settings for security-sensitive codebases?

✅ C) GitHub Copilot Enterprise
❓ This edition includes security-focused features like content exclusion to protect proprietary code.

13. You want Copilot to generate secure cryptographic implementations. Which of these approaches is the best?

✅ A) Specify ‘Use secure cryptographic methods’ in comments
❓ Providing security-focused guidance in comments helps Copilot suggest more robust encryption practices.

14. You have a function handling user authentication, and Copilot generates the following test case:

def test_authenticate_user():
assert authenticate("admin", "password123") == True

What is the most critical issue with this test?

✅ D) All of the above
❓ Hardcoded credentials pose a security risk, the test lacks edge cases, and negative test cases are missing, making it an inadequate test.

15. Which of the following GitHub Copilot security safeguards help mitigate accidental exposure of credentials in generated code?
(Choose two correct answers.)

✅ A) Copilot automatically detects API keys and removes them
❓ Copilot includes safeguards to prevent generating or exposing API keys in suggestions.

✅ D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials
❓ These filters reduce the risk of security vulnerabilities by blocking unsafe code patterns.

[GustavoBC2007]

I just wanted to take a moment to express my sincere gratitude for organizing this amazing learning campaign. The structured approach, engaging challenges, and insightful resources have truly made a difference in how I understand and use GitHub Copilot.

From mastering key features to exploring prompt engineering and security best practices, every week has been an opportunity to grow and enhance my skills. I also appreciate the chance to connect with a vibrant community of developers, share experiences, and push our learning even further.

A special thank you for the opportunity to earn certification vouchers 🎉—it’s a fantastic way to encourage deeper engagement and recognize our efforts!

Looking forward to continuing this journey and applying everything I’ve learned. Thanks again for your dedication and support! 🙌

[Akash1134]

Thanks for sharing your feedback @GustavoBC2007

[VCharrua]

And finally… 😊

I must say that I'm not exactly a young guy… I've been at this since the 90s with BASIC… I've seen the taxonomy of programming languages ​​evolve, from simple lines of imperative code to complex distributed systems. From editors as simple as a single cursor, to powerful IDEs like Visual Studio or fantastic online tools like GitHub CodeSpaces. I have gone through dozens of languages ​​and tools… and after several hundred thousand lines of code (even a little more), I still have the pleasure of being amazed by the evolution that only exists in this field that is our passion. 😂

The advances made with AI assistants for code generation are truly “game-changers” in what I have always considered an art… what we do and how we do-it has changed… it will have to change… From algorithms and advanced programming concepts, we have moved on to prompts and well-crafted specifications. From google and stack overflow, we changed to using chats and copilots. The transformative potential of these tools is truly extraordinary.

On a more personal note, and because in our field, those who don't evolve are left behind, I'm trying to integrate the power of this tool as much as possible into my development pipeline. I'm currently attending a fantastic GenAI Bootcamp offered by ExamPro (Andrew Brown), where the goal is to develop and use generative AI tools and capabilities. Although several AI code assistant solutions were presented, I chose to use GitHub Copilot exclusively to make the most of the resources provided by @Akash1134 and to put them into practice with a specific project. Although I'm also a little behind (partly because I want to dedicate as much as possible to this component), just finished pushing the backend component to my repository, which was entirely created by GitHub Copilot using only two specification files (in markdown) and a prompt to start the whole process.

I tried to make the most of GitHub Copilot's various features, whether through Copilot Chat or Copilot Edits. The help is amazing, even to the point of suggesting the text to include in my commits.
For those who want to take a look, here is the link to the repository (with a little ⭐ for you to come back 😃):

• VCharrua/free-genai-bootcamp-2025

I still have more than 50 tabs open with the fantastic documentation obtained during this activity and I will continue updating this project in the coming days (with the frontend being implemented in the next few hours).

Once again, my biggest thanks to the entire team involved in this challenge with a special shout out to @Akash1134. 🚀🚀🚀

[mvark]

Thanks for sharing your story @VCharrua

I agree that those who have worked with text editors will appreciate the new IDEs, the Intellisense of yesteryears and the almost magical AI code suggestions from a 24X7 guide like GitHub Copilot that we have now even more.

Copilot has inspired me to learn and implement projects that I had previously put on the back burner.

[RAVINDRA8008]

Here is my Answers

1️⃣ Ensuring Edge Case Coverage (Choose two)

• ✅ B) Provide explicit comments describing edge case scenarios before writing the function
• ✅ C) Use a combination of property-based testing and manually crafted assertions

---

2️⃣ Handling Floating-Point Precision Errors

• ✅ B) Modify assertions to account for precision tolerance

---

3️⃣ Following a TDD Workflow with Copilot

• ✅ A) Write failing test cases before implementing the function

---

4️⃣ Causes of Incorrect Test Generation

• ✅ B) Providing incomplete function definitions without parameter types
• ✅ C) Using Copilot with multiple test frameworks in the same file

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5️⃣ Debugging Efficiency with Copilot Chat (Choose two)

• ✅ A) Asking Copilot Chat to explain unexpected test failures
• ✅ C) Providing error logs to Copilot Chat for step-by-step troubleshooting

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6️⃣ Testing Large JSON Payloads

• ✅ B) Provide comments specifying ‘test with large JSON data’ before invoking Copilot

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7️⃣ Content Exclusion Settings in Copilot

• ✅ B) Restrict Copilot from suggesting code similar to private repositories

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8️⃣ Situations Leading to Insecure Code (Choose two)

• ✅ A) When Copilot is used without context-aware suggestions
• ✅ B) When Copilot generates code for outdated cryptographic practices like MD5 hashing

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9️⃣ Preventing Missed Integer Overflow Tests

• ✅ B) Explicitly add a comment describing edge cases, like integer overflows, before invoking Copilot

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🔟 Generating Correct Async Jest Tests

• ✅ B) Provide comments specifying ‘test async functions’

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1️⃣1️⃣ Missing Relevant Test Scenarios

• ✅ C) Copilot is missing necessary function comments or contextual prompts

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1️⃣2️⃣ Enterprise-Level Content Exclusion

• ✅ C) GitHub Copilot Enterprise

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1️⃣3️⃣ Generating Secure Cryptographic Implementations

• ✅ A) Specify ‘Use secure cryptographic methods’ in comments

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1️⃣4️⃣ Critical Issue in Authentication Test Case

• ✅ D) All of the above (🚨 Hardcoded credentials + No edge cases + No negative tests)

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1️⃣5️⃣ Security Safeguards Against Credential Exposure (Choose two)

• ✅ A) Copilot automatically detects API keys and removes them
• ✅ D) GitHub Copilot’s content filters prevent suggesting hardcoded credentials