GitHub Copilot Certification Exam Prep: Part Three - Prompt Engineering & Productivity 🎯

[Akash1134]

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👋 Welcome to Week 3 of the GitHub Copilot certification prep course! We’re halfway through the learning journey and so far we’ve seen some really thoughtful submissions and heard a lot of positive feedback from you all. Week 3 will push you a step closer to being exam-ready to ace your Copilot certification. This week we will be focusing on some of the foundational concepts of prompt engineering & productivity. You can always refer the comprehensive study guide.

• Prompt Crating and Prompt Engineering
  • Fundamentals of prompt crafting
  • Fundamentals of prompt engineering
• Developer use cases for AI
  • Improve developer productivity
  • How GitHub Copilot can help with SDLC
  • Limitations of using GitHub Copilot
  • How to use the productivity API to see how GitHub Copilot impacts coding

Note

Here’s your friendly nudge again: don’t forget—top participants will snag a GitHub Certifications exam voucher! 🎟️ It’s like finally merging that PR you’ve been perfecting. 🔁

◀️ Previous Modules:

• GitHub Copilot Certification Exam Prep: Part One - Let's Get Started ⏯️
• GitHub Copilot Certification Exam Prep: Part Two - Features and Data Handling ⚡
• Get certified with GitHub Copilot! Win exam vouchers & boost your skills 🚀

Week 3 ➡️ Step 1️⃣ — Build your base!

We’ve covered the basics of Copilot’s features and data handling, so now it’s time to learn the art of prompt creation and prompt engineering, helping you craft the best inputs for powerful AI outputs. We’ll also explore developer use cases, showing you how Copilot can make a real difference in your daily coding.

• Microsoft Learn Modules (each of the modules has its own knowledge check exercise)
  • Introduction to prompt engineering with GitHub Copilot
  • Developer use cases for AI with GitHub Copilot
    • Using GitHub Copilot with JavaScript (Optional)
    • Using GitHub Copilot with Python (Optional)
  • Implement code improvements using GitHub Copilot tools
  • Develop code features using GitHub Copilot tools
• GitHub Learning Pathways
  • Essentials of GitHub Copilot (series)
• 🤩 Microsoft Ignite 2024 (Bonus)
  • Maximize developer efficiency with GitHub Copilot for partner success
  • Top 5 GitHub Copilot Features at Microsoft Ignite 2024
  • Discover GitHub Copilot's untapped potential in Visual Studio
  • GitHub Copilot's Game Changing Features
• Videos
  • Master the core principles of prompt engineering with GitHub Copilot
  • Visual Studio Prompt Engineering with GitHub Copilot
  • GitHub Copilot Study Guide Series 2: Developer use cases for AI with GitHub Copilot
  • Level up with GitHub Copilot: using AI to learn, code, and build
  • Getting started with GitHub Copilot (Playlist with over 80 videos)
• Blogs and Resources
  • Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
  • What’s a prompt and what is prompt engineering?
  • What are the pros and cons of using GitHub Copilot?
  • How to use GitHub Copilot: Prompts, tips, and use cases
• GitHub Docs
  • Prompt engineering for GitHub Copilot
  • Use GitHub Copilot
  • Manage GitHub Copilot

Step 2️⃣ — Test Your Knowledge ⚡

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?

a) Statistical analysis and pattern recognition
b) Secure prompt transmission and context gathering
c) Content filtering to ensure safety and security
d) Context analysis to understand the user's intent

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

a) Clarity - Focus on a single, well-defined task.
b) Verbosity - Provide extensive and detailed descriptions.
c) Specificity - Use clear and explicit instructions.
d) Surround - Utilize descriptive filenames and keep related files open.

3. Identify which statement is valid and select the correct answer:

a) A prompt, which is our output, is a collection of songs that tells our copilot what to generate.
b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.
c) A prompt, which is our document, is a collection of laptops that tells our Copilot what to generate.
d) A prompt is a series of binary commands that control Copilot's behavior.

4. How can you accept GitHub Copilot's suggestions?

a) Press the Tab key.
b) Press the F1 key.
c) Press the F4 key.
d) Press the Enter key.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?

a) By providing code suggestions based on open-source libraries only
b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices
c) By suggesting code without considering the project context
d) By randomly generating code snippets

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?

a) Implicit prompts help enforce a consistent naming convention and syntax based on best practices within Visual Studio Code projects for improved readability.
b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
c) Implicit prompts help detect security vulnerabilities and potential malicious activities within Visual Studio Code projects for increased safety.
d) Implicit prompts automatically refactor your code to follow design patterns.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?

a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.
b) Using Copilot Chat as a replacement for human programming.
c) Ignoring secure coding and code review practices.
d) Asking Copilot Chat to generate entire applications without any guidance.

BONUS 👀

8. What is one way GitHub Copilot automates routine coding tasks for developers?

1. By deploying applications to production environments automatically.
2. By generating boilerplate code for common functionalities, such as setting up a REST API.
3. By managing database migrations and schema changes automatically.
4. By conducting performance testing and optimization of code automatically.

Engage in the discussion below by sharing your favorite study resources, providing feedback, and answering prep questions!

🔢 🔥 🚀

[Noblesse013]

ready to grind!

[Ronaldo-001]

Me too!!

[juanlujand]

Hi everyone
Beginning amazing week with interesting topics!! Keep rocking!

[Ronaldo-001]

Week 3 is fantastic for diving deep into prompt engineering concepts. GitHub Docs and Microsoft Learn are incredibly useful resources for this journey.
Special thanks to @Akash1134 Aakash for the guidance! 😊

This Week is so easy to answer all questions and recently learned to write markdown!!
Overall It's so fun!!

Questions and My Answers

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?

• Statistical analysis and pattern recognition
• Secure prompt transmission and context gathering
• Content filtering to ensure safety and security
• Context analysis to understand the user's intent
  Explanation: Secure prompt transmission relates to privacy measures, not the suggestion process.

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

• Clarity - Focus on a single, well-defined task.
• Verbosity - Provide extensive and detailed descriptions.
• Specificity - Use clear and explicit instructions.
• Surround - Utilize descriptive filenames and keep related files open.
  Explanation: Verbosity can confuse Copilot, so concise prompts are recommended.

3. Identify which statement is valid and select the correct answer:

• A prompt, which is our output, is a collection of songs that tells our Copilot what to generate.
• A prompt, which is our input, is a collection of instructions or guidelines that tell our Copilot what to generate.
• A prompt, which is our document, is a collection of laptops that tells our Copilot what to generate.
• A prompt is a series of binary commands that control Copilot's behavior.
  Explanation: Prompts are user inputs guiding Copilot's outputs.

4. How can you accept GitHub Copilot's suggestions?

• Press the Tab key.
• Press the F1 key.
• Press the F4 key.
• Press the Enter key.
  Explanation: We all know Tab key accepts the current suggestion.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?

• By providing code suggestions based on open-source libraries only
• By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices
• By suggesting code without considering the project context
• By randomly generating code snippets
  Explanation: Because Copilot adapts to the organization's coding style and practices.

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?

• Enforces consistent naming conventions and syntax.
• Improves responses without writing longer prompts.
• Detects security vulnerabilities.
• Automatically refactors your code.
  Explanation: Implicit prompts simplify interactions with Copilot.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?

• Limiting the prompt to coding questions or tasks to enhance the model's output quality.
• Using Copilot Chat as a replacement for human programming.
• Ignoring secure coding and code review practices.
• Asking Copilot Chat to generate entire applications without any guidance.
  Explanation: Focused questions improve output quality.

8. What is one way GitHub Copilot automates routine coding tasks for developers?

• Deploying applications to production environments automatically.
• Generating boilerplate code for common functionalities, such as setting up a REST API.
• Managing database migrations and schema changes automatically.
• Conducting performance testing and optimization of code automatically.
  Explanation: Copilot excels in generating boilerplate code to save time.

---

[cgonzalezdev]

Hi @Akash1134

Great start to the week #3. We're going for the voucher and the sky is the limit. 🔥 🚀

Answers with Explanations:

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
Answer: a) Statistical analysis and pattern recognition
Explanation: While GitHub Copilot relies on machine learning and neural networks, statistical analysis and pattern recognition are not explicitly part of the processing pipeline. The other options, such as secure prompt transmission, context analysis, and content filtering, are integral steps to ensure user intent is understood and code suggestions are safe and relevant.

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?
Answer: b) Verbosity - Provide extensive and detailed descriptions.
Explanation: Effective prompt engineering focuses on clarity and specificity rather than verbosity. Being overly verbose can confuse the model. Clear and concise instructions lead to better responses from GitHub Copilot.

3. Identify which statement is valid and select the correct answer:
Answer: b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.
Explanation: A prompt serves as the input to GitHub Copilot, containing clear instructions or context that guide the model to generate relevant code or text suggestions. Other options describe incorrect uses or interpretations of a "prompt."

4. How can you accept GitHub Copilot's suggestions?
Answer: a) Press the Tab key.
Explanation: In GitHub Copilot, pressing the Tab key confirms and accepts the suggested code snippet, integrating it into your work. Other keys listed are not used for this functionality.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
Answer: b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices.
Explanation: GitHub Copilot can adapt to an organization's coding style and standards, providing suggestions that align with established practices. This improves consistency and collaboration across projects.

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
Answer: b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
Explanation: Implicit prompts simplify interaction with Copilot, allowing developers to refine or debug code without the need for extensive descriptions. This feature makes fixing issues faster and more efficient.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
Answer: a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.
Explanation: By focusing on specific coding tasks or queries, developers can improve the relevance and quality of Copilot Chat's responses. Other practices, such as ignoring secure coding or attempting to replace human developers, are not recommended.

8. What is one way GitHub Copilot automates routine coding tasks for developers?
Answer: By generating boilerplate code for common functionalities, such as setting up a REST API.
Explanation: GitHub Copilot excels at creating boilerplate code, saving developers time on repetitive tasks like setting up APIs, database models, or configurations. This automation increases productivity by reducing manual coding efforts.

Leave me a like (heart) if you agree with my answers. 👀

Thanks again!!!

[Ronaldo-001]

Hi @cgonzalezdev ,
I have a small question. In your first question, it states that GitHub Copilot does not use statistical analysis, but I believe that might be incorrect for me. Could you please check and clarify this?

Thank you!

[cgonzalezdev]

Hi @cgonzalezdev , I have a small question. In your first question, it states that GitHub Copilot does not use statistical analysis, but I believe that might be incorrect for me. Could you please check and clarify this?

Thank you!

Please leave the explanation. If you have any suggestions, please leave me your argument as to what you consider to be a correct answer.

[Ronaldo-001]

Ok. I think the answer to the first question is:
b) Secure prompt transmission and context gathering.

[AronSoto]

Amazing Progress in Week 3 of GitHub Copilot Certification 🎉

This week has been incredible! Exploring the nuances of prompts and discovering various use cases across different programming languages has been a fascinating journey.

For the Bonus Question, here’s my take on what I believe is the most accurate answer.

---

• By generating boilerplate code for common functionalities, such as setting up a REST API.

Note

I understand that GitHub Copilot helps developers by leveraging AI to provide code suggestions and complete routine coding tasks, such as repetitive or repetitive code generation. Thus I believe, this functionality speeds up the development process and helps developers focus on more complex aspects of their projects.

[Vatsal-Sharma2003]

🚀 Week 3 Highlights: Prompt Engineering Made Simple! 🎉
This week was amazing for diving into the depths of Prompt Engineering! I explored concepts, played with Markdown, and found GitHub Docs & Microsoft Learn to be fantastic resources. Special shoutout to @Akash1134 for the guidance—it’s been so much fun! 😊

Here’s a quick Q&A recap to showcase what I’ve learned:

📝 Questions and Answers:
Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?

❌ Statistical analysis and pattern recognition
✅ Secure prompt transmission and context gathering
✅ Content filtering to ensure safety and security
✅ Context analysis to understand the user's intent
Explanation: Secure prompt transmission ensures privacy, but it’s not directly part of generating suggestions.
Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

✅ Clarity – Focus on a single, well-defined task.
❌ Verbosity – Provide extensive and detailed descriptions.
✅ Specificity – Use clear and explicit instructions.
✅ Surround – Utilize descriptive filenames and keep related files open.
Explanation: Verbosity can confuse Copilot—concise prompts work best!
Identify the valid statement:

❌ A prompt, which is our output, is a collection of songs that tells our Copilot what to generate.
✅ A prompt, which is our input, is a collection of instructions or guidelines that tell our Copilot what to generate.
❌ A prompt, which is our document, is a collection of laptops that tells our Copilot what to generate.
❌ A prompt is a series of binary commands that control Copilot's behavior.
Explanation: Prompts are input instructions that guide Copilot’s outputs.
How can you accept GitHub Copilot's suggestions?

✅ Press the Tab key.
❌ Press the F1 key.
❌ Press the F4 key.
❌ Press the Enter key.
Explanation: The Tab key accepts the current suggestion seamlessly.
How does Copilot use an organization's codebase and internal knowledge?

❌ By providing suggestions based on open-source libraries only.
✅ By tailoring coding assistance, answering questions, and aligning with the organization’s standards.
❌ By suggesting code without considering the project context.
❌ By randomly generating code snippets.
Explanation: Copilot adapts to an organization’s coding style and best practices, boosting productivity.
Benefits of implicit prompts with slash commands in inline chat:

✅ Improves responses without longer prompts.
❌ Enforces consistent naming conventions and syntax.
❌ Detects security vulnerabilities.
❌ Automatically refactors code.
Explanation: Implicit prompts simplify interactions and improve Copilot’s responses.
Recommended practice to improve Copilot Chat performance:

✅ Limit the prompt to coding tasks for enhanced output quality.
❌ Use Copilot Chat as a human programming replacement.
❌ Ignore secure coding and code review practices.
❌ Ask it to generate entire applications without guidance.
Explanation: Focused and clear questions produce the best results!
How does GitHub Copilot automate routine coding tasks?

❌ Deploys applications to production.
✅ Generates boilerplate code for common functionalities (e.g., setting up a REST API).
❌ Manages database migrations.
❌ Conducts performance testing and optimization.
Explanation: Copilot shines at automating repetitive coding tasks like generating boilerplate code.

[Ronaldo-001]

"Great recap! I love how you highlighted clarity over verbosity—concise prompts really do yield the best results.

[salahtidur]

the answers are:

1. a) Statistical analysis and pattern recognition
2. b) Verbosity - Provide extensive and detailed descriptions.
3. b) A prompt, which is our input, is a collection of instructions or guidelines that tell our Copilot what to generate.
4. a) Press the Tab key.
5. b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices
6. b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
7. a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.
8. no 2. By generating boilerplate code for common functionalities, such as setting up a REST API.

[geoffreywiseman]

Q 1️⃣

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?

a) Statistical analysis and pattern recognition

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 2️⃣

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

b) Verbosity - Provide extensive and detailed descriptions.

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 3️⃣

3. Identify which statement is valid and select the correct answer:

b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 4️⃣

4. How can you accept GitHub Copilot's suggestions?

a) Press the Tab key.

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 5️⃣

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?

a) Statistical analysis and pattern recognition

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 6️⃣

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?

b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

Covered in Introduction to Prompt Engineering with GitHub Copilot.

Q 7️⃣

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?

a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

Covered in Best practices for using GitHub Copilot.

Q 8️⃣

8. What is one way GitHub Copilot automates routine coding tasks for developers?

2 By generating boilerplate code for common functionalities, such as setting up a REST API

Covered in Developer Use Cases for AI with GitHub Copilot.

[sanskaarz]

Answers of week 3️⃣

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?

   • Answer: a) Statistical analysis and pattern recognition

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

   • Answer: b) Verbosity - Provide extensive and detailed descriptions.

3. Identify which statement is valid and select the correct answer:

   • Answer: b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

4. How can you accept GitHub Copilot's suggestions?

   • Answer: a) Press the Tab key.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?

   • Answer: b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices.

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?

   • Answer: b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?

   • Answer: a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

BONUS 👀

8. What is one way GitHub Copilot automates routine coding tasks for developers?
   • Answer: By generating boilerplate code for common functionalities, such as setting up a REST API.

Hi @Akash1134, the Microsoft Learn modules are fantastic! You've crafted the path to the GitHub certification exam preparation course brilliantly, and it's going great so far. 🪴

[Ramton03]

Here are the answers for questions.

1. a
2. b
3. b
4. a
5. b
6. b
7. a
8. 2

[1609033alumno]

Hi here my answers:

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
   a) Statistical analysis and pattern recognition

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

b) Verbosity - Provide extensive and detailed descriptions.(gg

3. Identify which statement is valid and select the correct answer:

b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

4. How can you accept GitHub Copilot's suggestions?
   a) Press the Tab key.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
   b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
   b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
   a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

BONUS 👀
8. What is one way GitHub Copilot automates routine coding tasks for developers?
By generating boilerplate code for common functionalities, such as setting up a REST API.

[juanlujand]

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
   a) Statistical analysis and pattern recognition

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

b) Verbosity - Provide extensive and detailed descriptions.

3. Identify which statement is valid and select the correct answer:

b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

4. How can you accept GitHub Copilot's suggestions?
   a) Press the Tab key.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
   a) By providing code suggestions based on open-source libraries only

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
   a) Implicit prompts help enforce a consistent naming convention and syntax based on best practices within Visual Studio Code projects for improved readability.
   b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
   Note: According to question, more than one answer.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
   a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

BONUS 👀
8. What is one way GitHub Copilot automates routine coding tasks for developers?

By generating boilerplate code for common functionalities, such as setting up a REST API.

[BODASROUR]

1-a
2-b
3-b
4-a
5-b
6-b
7-a
8-2

[BODASROUR]

1-a 2-b 3-b 4-a 5-b 6-b 7-a 8-2

• [ ]

• @

[selendemir]

Hello! Thank you for this week questions @Akash1134! Soo excited for the voucher and following through Microsoft Learn too!

1- Answer is A - because this is not Copilot function this is a technique used in machine learning.
2-Answer is B - because concise and specific - clean prompts are usually more effective.
3- Answer is B - this line describe the best -> A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.
4- Answer is A -> Press the Tab key. This is the way to accept a suggestion from GitHub Copilot.
5- Answer is B -> By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices is the exact way Copilot helping organizations.
6- Answer is B -> Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues. This is the top benefit of using them to simplify interactions with Copilot.
7- Answer is A -> It will improve the optimize Copilot Chat's performance.
8- Answer is the second one -> By generating boilerplate code for common functionalities, such as setting up a REST API. But I also use it sometimes for unit tests too.

[tan1193]

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
   Answer: a) Statistical analysis and pattern recognition
   Reason: GitHub Copilot uses context analysis, secure prompt transmission, and content filtering but does not explicitly involve statistical analysis in its operation.

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?
   Answer: b) Verbosity - Provide extensive and detailed descriptions.
   Reason: Verbosity is not an effective principle; clarity and specificity are prioritized to avoid overwhelming the model with unnecessary information.

3. Identify which statement is valid and select the correct answer:
   Answer: b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.
   Reason: A prompt is the user's input containing instructions or context to guide GitHub Copilot in generating code suggestions.

4. How can you accept GitHub Copilot's suggestions?
   Answer: a) Press the Tab key.
   Reason: The Tab key is the default way to accept suggestions from GitHub Copilot.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
   Answer: b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices
   Reason: GitHub Copilot leverages organizational context to provide relevant and standard-compliant code suggestions.

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
   Answer: b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
   Reason: Implicit prompts improve interaction by reducing the need for verbose input while maintaining context.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
   Answer: a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.
   Reason: Focused and specific queries lead to higher-quality responses and more effective interaction.

BONUS 👀
8. What is one way GitHub Copilot automates routine coding tasks for developers?
Answer: By generating boilerplate code for common functionalities, such as setting up a REST API.
Reason: GitHub Copilot is designed to save time by automating repetitive and standard coding tasks like creating boilerplate code.

[borhanitrash]

📝 Questions and Answers:

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
   Answer:

   a) Statistical analysis and pattern recognition.

   Explanation: GitHub Copilot does not directly use statistical analysis and pattern recognition in processing prompts. Instead, it leverages machine learning models to analyze the user's input (prompt), gather context, and provide suggestions securely and safely.

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?
   Answer:

   b) Verbosity - Provide extensive and detailed descriptions.

   Explanation: Effective prompt engineering focuses on clarity and specificity rather than verbosity. Overly long prompts can confuse the model, while concise and precise instructions improve response quality.

3. Identify which statement is valid and select the correct answer:
   Answer:

   b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

   Explanation: A "prompt" in GitHub Copilot is the input provided by the user. It includes instructions, guidelines, or context that the AI uses to generate relevant code or responses.

4. How can you accept GitHub Copilot's suggestions?
   Answer:

   a) Press the Tab key.

   Explanation: GitHub Copilot suggestions can be accepted by pressing the Tab key, which is the designated shortcut for confirming the suggested code.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
   Answer:

   b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices.

   Explanation: Copilot enhances productivity by providing contextually relevant suggestions aligned with the organization's internal codebase and best practices, ensuring consistency and collaboration.

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
   Answer:

   b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

   Explanation: Implicit prompts simplify interactions by using the context of the conversation or existing code, allowing developers to get concise yet accurate suggestions or fixes without creating elaborate prompts.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
   Answer:

   a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

   Explanation: Providing focused prompts enables GitHub Copilot to generate more accurate and relevant results. Using it as a supplement rather than a replacement for human input ensures better quality output.

BONUS 👀

8. What is one way GitHub Copilot automates routine coding tasks for developers?
   Answer:
   2. By generating boilerplate code for common functionalities, such as setting up a REST API.

   Explanation: GitHub Copilot automates routine tasks like generating boilerplate code, which saves developers time and reduces errors, allowing them to focus on complex problems.

It was my favorite week among the three, as I thoroughly enjoyed the experience of exploring prompt engineering. Thanks for opportunity @Akash1134

[Ronaldo-001]

Great insights! I totally agree prompt engineering is such a game-changer for getting precise output. For me too, this week has been amazing !

[davevad93]

Prompt Engineering & Productivity

1: Which of the following is NOT a step in the process of Copilot processing a user's prompt into a suggestion?

• A) Statistical analysis and pattern recognition

2: Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?

• B) Verbosity – Provide extensive and detailed descriptions.

3: Identify which statement is valid and select the correct answer:

• B) A prompt, which is our input, is a collection of instructions or guidelines that tell our Copilot what to generate.

4: How can you accept GitHub Copilot's suggestions?

• A) Press the Tab key.

5: How does Copilot use an organization codebase and knowledge to enhance productivity and collaboration?

• B) By tailoring coding assistance, answering questions, and aligning with the organization’s standards.

6: What are the benefits of using implicit prompts with slash commands for fixing code issues with Copilot?

• B) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

7: What is a recommended practice to improve the performance of GitHub Copilot Chat?

• A) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

Bonus: What is one way GitHub Copilot automates routine coding tasks for developers?

• 2) By generating boilerplate code for common functionalities, such as setting up a REST API.

[jccampanero]

@Akash1134 I think the Implement code improvements using GitHub Copilot tools link points to a wrong URL.

Could this be the right location instead?

https://learn.microsoft.com/en-us/training/modules/implement-code-improvements-using-github-copilot-tools/

I will try to post my answers as soon as possible1

Thank you very much for this week's contents!

[VCharrua]

Week 3 answers:

1. Which of the following is NOT a step in the process of GitHub Copilot processing a user's prompt into a code suggestion?
   a) Statistical analysis and pattern recognition

2. Which of the following is NOT a principle of effective prompt engineering for GitHub Copilot?
   b) Verbosity - Provide extensive and detailed descriptions.

3. Identify which statement is valid and select the correct answer:
   b) A prompt, which is our input, is a collection of instructions or guidelines that tell our copilot what to generate.

4. How can you accept GitHub Copilot's suggestions?
   a) Press the Tab key.

5. How does Copilot use an organization's codebase and internal knowledge to enhance productivity and collaboration?
   b) By tailoring coding assistance, answering questions, and suggesting code aligned with the organization's standards and best practices

6. What are the benefits of using implicit prompts with slash commands in inline chat for fixing code issues with GitHub Copilot?
   b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.

7. What is a recommended practice to improve the performance of GitHub Copilot Chat?
   a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.

8. What is one way GitHub Copilot automates routine coding tasks for developers?
   2. By generating boilerplate code for common functionalities, such as setting up a REST API.

[megheshshenoy3]

Thankyou @Akash1134 for another great quiz !!
Here are the answers for week 3:

1. a
2. b
3. b
4. a
5. b
6. b
7. a
8. 2

[adiazcan]

Question 8 is too ambiguos because, right now, we cannot automate tasks using Copilot.

1. A
2. B
3. B
4. A
5. B
6. B
7. A
8. D

[Ronaldo-001]

I Agree on Question 8! Copilot isn't about full automation yet, it helps with boilerplate code, which streamlines tasks. For me too, it felt a bit hard