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GitHub GitHub-Copilot Exam Syllabus Topics:

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

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GitHub CopilotCertification Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)

A. "Predict future coding trends and update my codebase accordingly."
B. "Suggest ways to enhance the maintainability of this code segment."
C. "Show me how to improve the readability of this function."
D. "Refactor my application to meet the latest coding standards."

Answer: B,C

Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.

NEW QUESTION # 88
Why is code reviewing still necessary when using GitHub Copilot to write tests?

A. Because GitHub Copilot replaces the need for manual testing.
B. Because GitHub Copilot can cover all possible scenarios in your test cases.
C. Because GitHub Copilot's generated test cases may not cover all possible scenarios.
D. Because GitHub Copilot generates the best code possible for the test scenario.

Answer: C

Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.

NEW QUESTION # 89
What is few-shot prompting?

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

Answer: D

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 # 90
How can you get multiple suggestions from GitHub Copilot?

A. By using @workspace in the chat window
B. By using the inline chat functionality with the command 'multiple'
C. By asking for multiple suggestions using comments in your code
D. By opening the completions panel in your editor

Answer: D

Explanation:
You can get multiple suggestions by opening the completions panel in your editor, which displays alternative code suggestions.

NEW QUESTION # 91
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

A. Changes to content exclusion settings
B. Accepted chat suggestions
C. Suggestions blocked by duplication detection filtering
D. Code suggestions made by GitHub Copilot

Answer: A

NEW QUESTION # 92
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