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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.

Topic 3

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 4

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.

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GitHub CopilotCertification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
What is the correct way to access the audit log events for GitHub Copilot Business?

A. Navigate to the Security tab in the organization's GitHub settings
B. Use the Audit log section in the organization's GitHub settings
C. Use the Code tab in the GitHub repository
D. Navigate to the Insights tab in the repository settings

Answer: B

Explanation:
Audit log events for GitHub Copilot Business can be accessed through the Audit log section within the organization's GitHub settings. This log provides a record of activities related to Copilot usage and configuration.

NEW QUESTION # 27
Which Microsoft ethical AI principle is aimed at ensuring AI systems treat all people equally?

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

Answer: B

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

NEW QUESTION # 28
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

A. It primarily relies on the model's training data to generate responses.
B. It combines its training data set, code in user repositories, and external sources like Bing to generate responses.
C. It primarily uses search results from Bing to generate responses.
D. It uses user-provided documentation exclusively to generate responses.

Answer: B

Explanation:
GitHub Copilot Chat combines its training data, code from user repositories, and external sources like Bing to generate comprehensive and relevant responses to coding questions.

NEW QUESTION # 29
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

A. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.
B. GitHub Copilot can design new statistical methods that have not been previously documented.
C. GitHub Copilot can independently verify the statistical significance of results.
D. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.

Answer: D

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.

NEW QUESTION # 30
What is the correct way to exclude specific files from being used by GitHub Copilot Business during code suggestions?

A. Add the specific files to a copilot.ignore file.
B. Modify the .gitignore file to include the specific files.
C. Use the GitHub Copilot settings in the user interface to exclude files.
D. Rename the files to include the suffix _no_copilot.

Answer: C

Explanation:
The correct way to exclude files is through the GitHub Copilot settings in the user interface, which allows administrators to specify files and directories to be ignored.

NEW QUESTION # 31
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