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!!

[dpewaal-iba]

How do you plan to stay motivated during the "grind"?

[Ronaldo-001]

I plan to stay motivated by setting small, achievable goals each week and celebrating those wins. Keeping the end goal in sight, staying consistent, and engaging with this amazing community will definitely keep me going. How about you?

[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

[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 !

[Trophalaxeur]

Late but still there ^^

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

[alesbednarik]

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

[Salgado2004]

Hello guys!!

Sorry for the delayed response, this week's been rough... 😅 but anyway, once again I'm here to share my thoughts about the contents and materials, based (of course) in the exam study guide!

Prompt engineering and the GitHub Copilot best practices is really interesting to talk about! I have to say... I wasn't following all the best practices 😝 now is a great time to start! How about you? Were you following the best practices? Or did you learn about them this week?

Week Three: Breakdown

Domain 4: Prompt crafting and Prompt engineering 9%

Describe the fundamentals of prompt crafting

• Describe how the context for the prompt is determined

  Like I said last week, Copilot uses different strategies to gather context from the environment, like relevant code snippets, advanced tecniques like FIM, file paths, comments from the opened files in the IDE, etc. It's pretty complete.

• Describe the different parts of a prompt

  If I get this correctly, essentially a prompt has two parts or stages:

  1. The developers stage, when the prompt consists in code snippets or natural language comments that contains instructions to generate a specific sugestion
  2. The LLM stage, when the prompt is enriched with context and session data to be processed by the LLM

• Describe the role of prompting

  I like to think of prompting as a way to organize what you need to do in a solution, and then giving it to an AI. Prompting is how you get the full features from AI's code generation, enhancing the quality of code suggestions.

• Describe the difference between zero-shot and few-shot prompting

  Zero-shot and few-shot are two opposite strategies to prompt copilot. While zero-shot relies solely on writing a comment (following the principles and best practices) to tell copilot what to do, few-shot provides comments and examples for input and output. I believe each of them have their own use-cases. Although few-shot probably returns more precise answers, sometimes zero-shot is enough to get what you need.

• Describe the way chat history is used with GitHub Copilot

  Historical data in the Copilot Chat used outside the code editor can be retained for 28 days, it can include prompts, suggestions, and supporting context. The retained data is used to enable conversational interactions with the AI, allowing the user to ask follow-up questions and requesting clarifications, using the chat history as context.

• Identify prompt crafting best practices when using GitHub Copilot

  GitHub Copilot works better when you provide useful information that can be used for learning. So when crafting prompts, including examples e clear specifications can improve the quality of the answers. Also, the iteration of dialogues with Copilot can enrich the suggestions, by adjusting your prompts untill they fill what you need. Another tip is to avoid ambiguity, like using 'this' to refer to something in the code. (And you can always say 'Please' in the end of your sentence 😉)

Describe the fundamentals of prompt engineering

• Explain prompt engineering principles, training methods, and best practices

  The principles are called 4 Ss. They are "Single", "Specific", "Short" and "Sorround".

  • Single says that you should focus your prompt on a single task;
  • Specific says that should ensure that the instructions are detailed and explicit;
  • Short says that your should write the instructions straight to the point, being concise and clear (Note that it needs to balance with Specific)
  • Surround says that you should provide rich context to AI, such as keeping related files open and use descriptive names

• Describe the prompt process flow

  The prompt process flow is really similar to the sugestions lifecycle from last week. But it summarizes some steps, and divides the flow between inbound and outbound. I believe it's important to understand that it uses an API to send the prompt for copilot, it goes throught a filter proxy, the LLM process and generates the response, the response is also filtered in the proxy and returned to us.

Domain 5: Developer use cases for Al 14%

Improve developer productivity

• Describe how AI can improve common use cases for developer productivity
  AI can improve developers productivity in several ways, for example:
  • Accelerate learning new programming languages: The programming rarely changes, and can be used as a base for many common languages out there, what takes more time to learn is sintax and coding styles, that Copilot can suggest and explain.
  • Minimizing context switching: Copilot can help you avoid leaving the IDE to search for the correct sintax for stream().reduce() in java (I always forget), and getting assistance.
  • Documentation writing: Who doesn't go blank when it needs to write documentation or descriptive comments in the code?
  These are only some of the examples, with AI in the workday, you'll find many more ways to get help and improve your coding rotine.

• Discuss how GitHub Copilot can help with SDLC (Software Development Lifecycle) management

  GitHub can has the hability to impact all the phases of SDLC in different ways.

  • Although Copilot can't directly help in the Requirement analysis stage, it can accelerate the beginning of implementation, from high-level descriptions
  • During the Design and Development stages, Copilot will shine with code completions and suggestions to improve productivity and satisfaction of developers, saving them from writing boilerplate code, for example
  • It's also very helpful in the Testing and quality assurance stage, generating unit tests for functions in the code or quickly creating mock data for testing
  • Even though I love GitHub Actions, it's not very easy to debug it when it has a problem. But with Copilot, the Deployment gets a lot easier, because it can identify configuration errors or that tab that was missing
  • And last but not least, Copilot can accelerate code refactorings and bug fixes in the Maintenance and Support stage.

• Describe the limitations of using GitHub Copilot

  We've already talked about this last week (and the previous one too), but it's important to understand. In the end, AI goes back to statistics. That means that suggestion might have biases from training sets, vary from language to language (more popular languages tend to get better results), not meet the requirements, not adhere security practices, etc. It's a very powerful autocomplete, but it's not 100% accurate. That's why it's important to review the code generated.

• Describe how to use the productivity API to see how GitHub Copilot impacts coding

  Managers like metrics, and copilot understands that. GitHub Copilot provides a Rest API to gather usage metrics for enterprise members, teams and organization members. Alongside with the API, GitHub also suggests the implementation of a framework to systematically interpret the metric results and use copilot the best way possible

Prep Questions

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?

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

Note

• I'm glad I already followed some of the best practices 😅 but there are some that I was not aware about! I'm excited
  to try them out and see the results
• In the exam guide there's a topic "Describe the language options for promoting GitHub Copilot"... I didn't get
  what is this supposed to mean, can anybody help me?
• Please, does anybody know if the productivity API is available for Copilot Individual? It looks like a
  feature for
  Business and Enterprise

[Ronaldo-001]

Wow, this is such a detailed and insightful breakdown! 🙌 I really appreciate how you've structured the explanation of prompt engineering and Copilot's use cases—it makes it so much easier to understand.

[FranciscoLDB]

Here we go to week 3 🚀 🏃🏻

Week 3 is crucial! Excited to dive deeper into prompt engineering with GitHub Copilot. The modules and resources are super helpful for exam prep.

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) Helps get better responses without writing longer prompts, simplifying interactions.

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

a) Limit prompts to coding questions or tasks to enhance output quality.

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

b) Generating boilerplate code for common functionalities, such as setting up a REST API.

[spectraneo]

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
5. b) Implicit prompts help get better responses from GitHub Copilot without writing longer prompts, making it easier to interact and fix code issues.
6. a) Limiting the prompt to coding questions or tasks to enhance the model's output quality.
7. By generating boilerplate code for common functionalities, such as setting up a REST API.

[PETAPAMENMESJJ]

1, c. Content filtering to ensure safety and security.
2, d. Surround- utilize descriptive filenames and keep related files open.
3, b. A prompt, which is our inputs is 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 prompts to coding questions or tasks to enhance the model's output quality.
8, by conducting performance testing and optimization of code automatically.

[Noblesse013]

I made this video on how we can interact with GitHub Copilot ) with this template repo Link. Let's do it along with me!

[Ronaldo-001]

That's awesome! . Can't wait to check it out and try it alongside you with the template repo. Keep up the amazing work!

[Noblesse013]

So, here's my answer for the week-03:

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: While Copilot relies on machine learning and pattern recognition, it does not directly perform "statistical analysis" as a step.

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: Prompts should be clear and specific, not overly verbose, to help Copilot generate accurate suggestions.

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: Prompts are the input provided to guide the output from tools like Copilot.

4. How can you accept GitHub Copilot's suggestions?
   Answer: a) Press the Tab key.
   Reason: The Tab key is commonly used to accept suggestions provided by 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: Copilot can adapt its suggestions to align with an organization's context and coding standards.

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 streamline interaction with Copilot, improving efficiency.

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: Keeping prompts concise and focused improves the relevance of the output.

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: Copilot is often used to create boilerplate code to save developers time.

[ButeoButeo]

I'm a bit late but I am still here :) Here are 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.

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

[xoraht]

I forgot to put my answers :O

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.

[2mdtln]

My Answers for Week 3:

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: b) By generating boilerplate code for common functionalities, such as setting up a REST API

[danlegilisho]

Week 3 responses

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

[akramkhan0889]

Q1 - A
Q2 - B
Q3 - C
Q4 - A
Q5 - B
Q6 - B
Q7 - A
Q8 - B

my exam is scheduled for tomorrow :)

[Ronaldo-001]

Good Luck!!

[queenofcorgis]

Week 4 of the GitHub Copilot certification prep course is now Live!
This week, we’re diving deep into testing, security, and privacy for GitHub Copilot—your final chance to level up! 🍄 Let’s make this last week of the prep journey count and finish strong! 💪🚀

[mvark]

Thanks so much for the curated list of resources which includes the YouTube playlist of 81 videos and especially the reasoning for the Practice questions' answers

The Visual Studio Code YouTube Channel also offers excellent videos on GitHub Copilot, such as 'Essential AI Prompts for Developers' (8 minutes), which shares valuable tricks that might be difficult to discover on your own.

The book "Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT" was my introduction to Python & Github Copilot and I highly recommend it

[kiranreddi]

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