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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.

Topic 2

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 3

Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.

Topic 4

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 5

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 6

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.

GitHub CopilotCertification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
How does GitHub Copilot Chat ensure that a function works correctly?

A. By executing the test cases to validate the correctness of the code.
B. By automatically writing all the tests for the function.
C. By suggesting assertions based on the code's context and semantics.
D. By writing the implementation code for the function.

Answer: C

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 # 12
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

A. Comparing suggestions against public code using machine learning.
B. Analyzing the context and structure of the code being written
C. Reviewing the user's browsing history to identify public repositories
D. Running code suggestions through filters designed to detect public code

Answer: D

Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.

NEW QUESTION # 13
Select a strategy to increase the performance of GitHub Copilot Chat.

A. Apply prompt engineering techniques to be more specific
B. Use a single GitHub Copilot Chat query to find resolutions for the collection of technical requirements
C. Optimize the usage of memory-intensive operations within generated code
D. Limit the number of concurrent users accessing GitHub Copilot Chat

Answer: A

Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.

NEW QUESTION # 14
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?

A. Inclusiveness
B. Accountability
C. Transparency
D. Fairness

Answer: C

Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrivesat its decisions and suggestions.

NEW QUESTION # 15
Which of the following describes role prompting?

A. Giving GitHub Copilot multiple examples of the form of the data you want to use
B. Prompt GitHub Copilot to explain what was the role of a suggestion
C. Tell GitHub Copilot in what tone of voice it should respond
D. Describing in your prompt what your role is to get a better suggestion

Answer: D

Explanation:
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.

NEW QUESTION # 16
......

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Jack Ford Jack Ford

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