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GitHub GitHub-Copilot Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GitHub Copilot Plans and Features	31%	- Core Copilot features 1. CLI and multi-suggestion workflows 2. Inline code suggestions 3. Chat-based assistance - Copilot plans comparison 1. IDE integration and Copilot Chat usage 2. Copilot Individual vs Business vs Enterprise
Topic 2: GitHub Copilot Data, Privacy, and Architecture		- Data handling and privacy safeguards 1. Content exclusions and enterprise controls 2. Data usage and retention considerations
Topic 3: Prompt Engineering and Context Crafting		- Effective prompting techniques 1. Context optimization for code generation 2. Zero-shot and few-shot prompting
Topic 4: Responsible AI	7%	- Explain responsible usage of AI 1. Risks and limitations of generative AI 2. Validating AI-generated output 3. Bias, privacy, security, and transparency considerations 4. Ethical AI practices and mitigation of harm
Topic 5: Developer Productivity with Copilot		- Workflow optimization 1. Debugging and code completion enhancement 2. Using Copilot in IDE workflows

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GitHub CopilotCertification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
What is few-shot prompting?

A. Telling GitHub Copilot to iterate several times on the answer before returning it to you
B. Telling GitHub Copilot about the mechanism you want it to use and how to incorporate that into the response
C. Telling GitHub Copilot from which sources it should base the response on
D. Telling GitHub Copilot to try multiple times to answer the prompt

Answer: B

Explanation:
By embedding a few concrete examples of the task - showing sample inputs alongside the exact outputs you expect - you guide Copilot to generalize from those instances and produce more accurate code for similar cases.

NEW QUESTION # 65
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

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

Answer: C

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 # 66
How does GitHub Copilot Enterprise assist in code reviews during the pull request process?
(Select two.)

A. It can answer questions about the changeset of the pull request.
B. It automatically merges pull requests after an automated review.
C. It generates a prose summary and a bulleted list of key changes for pull requests.
D. It can validate the accuracy of the changes in the pull request.

Answer: A,C

Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.

NEW QUESTION # 67
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

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

Answer: B,D

Explanation:
To improve prompt quality for GitHub Copilot (especially Copilot Chat), good prompts should:
Clearly describe what "success" looks like
Specify where the context comes from (e.g., which file, function, or problem area) Be focused, not overloaded with unnecessary information

NEW QUESTION # 68
When using GitHub Copilot Chat to generate unit tests, which slash command would you use?

A. /generate-tests
B. /tests
C. /init-tests
D. /create-tests

Answer: A

Explanation:
The /generate-tests slash command instructs Copilot Chat to create unit tests for the selected code.

NEW QUESTION # 69
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