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Microsoft GH-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use GitHub Copilot responsibly	15%	- Copilot responsible operation 1. Safe usage practices in development workflows - Responsible AI principles 1. Risks and limitations of generative AI 2. Validation of AI-generated output 3. Ethical AI usage and governance
Topic 2: Privacy, exclusions, and safeguards	15%	- Security safeguards 1. Security warnings and mitigations 2. Duplication detection - Privacy configuration 1. Data ownership and usage limitations 2. Content exclusions and repository controls
Topic 3: Improve developer productivity with Copilot	10%	- Software development enhancement 1. Code generation and refactoring 2. Legacy code modernization 3. Sample data generation - Testing and security 1. Unit and integration test generation 2. Security and performance suggestions
Topic 4: Understand GitHub Copilot data and architecture	15%	- Data handling 1. Data flow and privacy boundaries 2. Input processing and prompt construction - System behavior and limitations 1. Suggestion lifecycle 2. LLM limitations
Topic 5: Prompt engineering and context crafting	10%	- Prompt design 1. Zero-shot and few-shot prompting 2. Prompt structure and context usage - Optimization 1. Prompt refinement techniques
Topic 6: Use GitHub Copilot features	30%	- Organization administration 1. Policy management and feature control 2. Audit logs and subscription management - IDE integration 1. File and repository exclusions 2. CLI usage and setup 3. Inline suggestions and chat usage - Advanced Copilot capabilities 1. Pull request summaries and code review 2. Agent Mode and Edit Mode 3. Copilot Spaces and Spark

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Microsoft GitHub Copilot Sample Questions (Q24-Q29):

NEW QUESTION # 24
When using an IDE with a supported GitHub Copilot plug-in, which Chat features can be accessed from within the IDE? (Each correct answer presents part of the solution. Choose two.)

A. Explain code and suggest improvements
B. Find out about releases and commits
C. Generate unit tests
D. Plan coding tasks

Answer: A,C

Explanation:
Exact extracts:
* "In supported IDEs, you can ask Copilot Chat to explain code or suggest improvements." References: GitHub Copilot Chat IDE integration documentation.

NEW QUESTION # 25
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?

A. Suggestions matching public code to 'blocked'
B. Enable GitHub Copilot license checking
C. Enforce blocking of MIT or GPL licensed code
D. You need to check code suggestions yourself before accepting

Answer: A

Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.
Reference: GitHub Copilot documentation on IP protection and code filtering.

NEW QUESTION # 26
Select a strategy to increase the performance of GitHub Copilot Chat.

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

Answer: D

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 # 27
What method can a developer use to generate sample data with GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. Utilize GitHub Copilot's capability to directly access and use databases to create sample data.
B. Leveraging GitHub Copilot's ability to independently initiate and manage data storage services.
C. Leveraging GitHub Copilot's suggestions to create data based on API documentation in the repository.
D. Utilizing GitHub Copilot's ability to create fictitious information from patterns in training data.

Answer: C,D

Explanation:
GitHub Copilot can generate sample data by creating fictitious information based on patterns in its training data and by using suggestions based on API documentation within the repository.
Reference: GitHub Copilot documentation on data generation assistance.

NEW QUESTION # 28
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)

A. Creation of end-to-end performance testing scenarios for a web application
B. Creation of a unit test scenario for newly developed Python code
C. Explain a legacy COBOL code and translate the code to another language like Python.
D. Create a technical requirement specification from the business requirement documentation

Answer: B,C

Explanation:
GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.
Reference: GitHub Copilot Chat use cases.

NEW QUESTION # 29
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