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Microsoft GH-300 Exam Syllabus Topics:
Topic
Details
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.
Topic 2
- 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.
Topic 3
- Domain 6: Testing with GitHub Copilot: This section measures abilities of QA Engineers and Test Automation Specialists to use Copilot for test generation, including unit and integration tests. It explains how Copilot can identify edge cases and assist in writing assertions. The domain also covers different Copilot subscription SKUs, privacy considerations, organizational code suggestion settings, and configuration files related to Copilot.
Topic 4
- Domain 4: Prompt Crafting and Prompt Engineering This section measures skills of Software Developers and AI Interaction Designers in effectively crafting prompts to optimize Copilot’s output. It reviews foundational concepts such as prompt components, the role of language in prompting, zero-shot vs. few-shot prompting, and how chat history influences responses. Best practices and engineering principles for prompt design and training methods are also covered.
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Microsoft GitHub Copilot Sample Questions (Q122-Q127):
NEW QUESTION # 122
What specific function does the /fix slash command perform?
- A. Proposes changes for detected issues, suggesting corrections for syntax errors and programming mistakes.
- B. Converts pseudocode into executable code, optimizing for readability and maintainability.
- C. Generates new code snippets based on language syntax and best practices.
- D. Initiates a code review with static analysis tools for security and logic errors.
Answer: A
Explanation:
"/fix: Propose changes to resolve detected issues, such as syntax errors or common programming mistakes, in the selected code." This confirms that the /fix command is used to suggest corrections for identified problems.
References: GitHub Copilot Chat command reference.
NEW QUESTION # 123
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)
- A. By using the "/fix" command in GitHub Copilot in-line chat.
- B. By highlighting the code you want to fix, right-clicking, and selecting "Fix using GitHub Copilot."
- C. By running the gh copilot fix command.
- D. By adding comments to your code and triggering a suggestion.
- E. By highlighting the code you want to fix, right-clicking, and selecting "Refactor using GitHub Copilot."
Answer: D,E
Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting "Refactor using GitHub Copilot" from the context menu.
NEW QUESTION # 124
What is zero-shot prompting?
- A. Only giving GitHub Copilot a question as a prompt and no examples
- B. Giving GitHub Copilot examples of the problem you want to solve
- C. Giving as little context to GitHub Copilot as possible
- D. Telling GitHub Copilot it needs to show only the correct answer
- E. Giving GitHub Copilot examples of the algorithm and outcome you want to use
Answer: A
Explanation:
"Zero-shot prompting means asking the model to perform a task without providing examples-only the question or task description is given." This confirms that the correct definition of zero-shot prompting is providing no examples, only a direct prompt.
References: GitHub Copilot and AI prompting documentation.
NEW QUESTION # 125
How does GitHub Copilot Chat ensure that a function works correctly?
- A. By writing the implementation code for the function.
- B. By suggesting assertions based on the code's context and semantics.
- C. By automatically writing all the tests for the function.
- D. By executing the test cases to validate the correctness of the code.
Answer: B
Explanation:
GitHub Copilot Chat can suggest assertions based on the code's context and semantics to help developers verify the correctness of their functions. These assertions serve as checks that the function behaves as expected under various conditions.
NEW QUESTION # 126
When using GitHub Copilot Chat to generate boilerplate code for various test types, how can you guide the AI to follow the testing standards of your company?
- A. By using a specific command in the terminal.
- B. By using a specific setting in GitHub Copilot's configuration.
- C. By using specific prompt examples in your chat request.
- D. By using a specific slash command in the prompt.
Answer: C
Explanation:
"The quality of Copilot Chat responses depends on the clarity and specificity of your prompts. You can guide Copilot to follow organizational standards by including examples or requirements in your request." This means prompt engineering with specific examples is the way to align with company standards.
References: GitHub Copilot Chat best practices documentation.
NEW QUESTION # 127
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