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| Topic 1 | - How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.
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| Topic 2 | - Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.
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| Topic 3 | - Responsible :This section of the exam measures skills of AI Ethics Officers and Risk Managers and covers the responsible and ethical usage of AI technologies. It explains the risks and limitations associated with generative AI tools, including biases in training data and the need to validate AI outputs. Candidates learn how to operate AI responsibly by identifying potential harms such as bias, fairness, privacy concerns, and mitigating these harms by applying ethical AI principles.
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Microsoft GitHub Copilot Sample Questions (Q81-Q86):
NEW QUESTION # 81
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?
- A. GitHub Copilot Business
- B. GitHub Copilot Individual
- C. GitHub Copilot Teams
- D. GitHub Copilot Enterprise
- E. GitHub Copilot Business for non-GHE Customers
Answer: B
Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.
NEW QUESTION # 82
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?
- A. GitHub Copilot Business
- B. GitHub Copilot Individual
- C. GitHub Copilot Teams
- D. GitHub Copilot Enterprise
- E. GitHub Copilot Business for non-GHE Customers
Answer: B
Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.
Reference: GitHub Copilot pricing documentation.
NEW QUESTION # 83
How do you generate code suggestions with GitHub Copilot in the CLI?
- A. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
- B. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.
- C. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.
- D. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
Answer: D
Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.
NEW QUESTION # 84
What is few-shot prompting?
- A. Telling GitHub Copilot from which sources it should base the response on
- B. Telling GitHub Copilot about the mechanism you want it to use and how to incorporate that into the response
- C. Telling GitHub Copilot to try multiple times to answer the prompt
- D. Telling GitHub Copilot to iterate several times on the answer before returning it to you
Answer: B
Explanation:
"Few-shot prompting is a technique where you provide examples of the mechanism or output you want the model to follow. These examples guide the AI's responses." This proves that option A is correct.
References: GitHub Copilot prompt engineering documentation.
NEW QUESTION # 85
You use GitHub Copilot Chat to debug a function.
You discover that GitHub Copilot is using irrelevant chat threads to generate output.
You need to ensure that GitHub Copilot only uses new input to generate output.
What should you do, and how will this affect the prompt process flow?
- A. Start a new chat in GitHub Copilot Chat; the previous chat history will not be included in the new model input.
- B. Continue the existing chat; the previous chat history will be removed from the model input.
- C. Start a new chat in GitHub Copilot Chat; the previous chat history will remain included in the model input.
- D. Continue the existing chat; the previous chat history will remain included in the model input.
Answer: A
Explanation:
GitHub Copilot Chat uses conversation history as part of the context for later responses. GitHub ' s prompt- engineering guidance recommends keeping chat history relevant, while GitHub ' s AI-usage guidance specifically advises starting a new conversation when moving to unrelated work because a long thread carries prior history into subsequent requests. In this scenario, irrelevant earlier chat threads are contaminating the context used for debugging. Starting a new Copilot Chat session creates a clean conversational context for the new prompt, so the previous thread is no longer included as model input for that new conversation.
Continuing in the same chat would preserve the unwanted context. Therefore, the correct action is to start a new chat, with the effect of removing the prior chat history from the new model input.
NEW QUESTION # 86
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