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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.

Topic 2

Privacy Fundamentals and Context Exclusions: This section of the exam measures skills of Cybersecurity Specialists and Compliance Officers and covers privacy safeguards and content exclusion settings in GitHub Copilot. It explains how Copilot can identify security vulnerabilities, suggest optimizations, and enforce secure coding practices. It also includes details on content ownership, data filtering mechanisms, and exclusion configurations. The section concludes with troubleshooting guidelines for managing context exclusions and ensuring compliance with organizational security policies.

Topic 3

How GitHub Copilot Works and Handles Data: This section of the exam measures the skills of Data Security Specialists and DevOps Engineers and covers how GitHub Copilot processes data, handles code suggestions and manages privacy concerns. It explains the data pipeline for Copilot’s suggestions, how it gathers context, and how prompts are processed through its AI model. The section also discusses the limitations of AI-generated code, the effects of historical data on suggestions, and the role of prompt crafting. Best practices for improving prompt effectiveness and optimizing AI-generated responses are included.

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GitHub CopilotCertification Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
What are the effects of content exclusions? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot suggestions are no longer available in the excluded files.
B. The excluded content is no longer used while debugging the code.
C. The IDE will not count coding suggestions in the excluded content.
D. The excluded content is not directly available to GitHub Copilot to use as context.

Answer: A,D

Explanation:
Content exclusions prevent GitHub Copilot from using the excluded content as context and stop suggestions from being generated in those files.

NEW QUESTION # 45
Which Microsoft ethical AI principle is aimed at ensuring AI systems treat all people equally?

A. Reliability and Safety
B. Fairness
C. Privacy and Security
D. Inclusiveness

Answer: B

Explanation:
The principle of fairness ensures that AI systems treat all people equally and avoid discriminatory outcomes.

NEW QUESTION # 46
How does GitHub Copilot suggest code optimizations for improved performance?

A. By enforcing strict coding standards that ensure optimal performance.
B. By automatically rewriting the codebase to use more efficient code.
C. By providing detailed reports on the performance of the codebase.
D. By analyzing the codebase and suggesting more efficient algorithms or data structures.

Answer: D

Explanation:
GitHub Copilot suggests code optimizations by analyzing the codebase and recommending more efficient algorithms or data structures.

NEW QUESTION # 47
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

A. Provide specific success criteria
B. Provide all information about the utilized files
C. Provide links to supporting documentation
D. Provide insight on where to get the content from to get a response

Answer: A,C

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.

NEW QUESTION # 48
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

A. The Gen AI tool is programmed with a focus on creativity over factual accuracy.
B. The Gen AI tool is experiencing downtime and is not fully recovered.
C. The Gen AI tool is overloaded with too many requests at once.
D. The training data might contain biases or inconsistencies.

Answer: D

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.

NEW QUESTION # 49
......

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Peter Harris Peter Harris

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