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Microsoft GH-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prompt Engineering and AI Interaction9%- Improving Copilot responses using context
- Creating effective prompts
- Refining prompts for better coding results
- Crafting and refining prompts (prompt engineering)
Topic 2: How GitHub Copilot Works and Handles Data15%- Privacy and data handling within Copilot
- AI model response generation
- How Copilot gathers code context
- The prompt creation process
- Understanding that private repository content is not used to train models
Topic 3: Privacy Fundamentals and Context Exclusions15%- Safeguards: duplication detector, contractual protection, data collection settings
- Ownership of AI-generated outputs
- Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot
- Content exclusions at repository and organization level
- Effects and limitations of content exclusions
Topic 4: Developer Use Cases for AI14%- Documentation generation
- Debugging applications
- Automating repetitive development tasks
- Learning new languages and frameworks
- SDLC support and productivity insights
- Code generation and refactoring
Topic 5: GitHub Copilot Plans and Features31%- Configuration options and productivity features
- Differences between Copilot Individual, Business, and Enterprise plans
- Implementing and managing Copilot plans for an organization
- IDE integration (VS Code, Visual Studio, JetBrains)
- Copilot Chat and code suggestion capabilities
Topic 6: Testing with GitHub Copilot9%- Editor configuration and org-level suggestion settings
- Improving test coverage with AI suggestions
- Generating unit tests and integration tests
- Identifying edge cases
Topic 7: Responsible AI with GitHub Copilot7%- Validating AI output and mitigation strategies
- Bias and fairness in AI-generated code
- Risks and limitations of generative AI tools
- Privacy and transparency considerations
- Ethical AI principles and responsible use

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Microsoft GitHub Copilot Sample Questions (Q114-Q119):

NEW QUESTION # 114
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.)

Answer: C,D

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.
Reference: GitHub Copilot prompt engineering best practices.


NEW QUESTION # 115
What content can be configured to be excluded with content exclusions? (Each correct answer presents part of the solution. Choose three.)

Answer: B,C,D

Explanation:
"Enterprise administrators can configure content exclusions to exclude entire repositories, folders, or specific files from being used by Copilot." This confirms that files, folders, and repositories can be excluded, but not individual lines or gists.
References: GitHub Copilot content exclusions documentation.


NEW QUESTION # 116
How can GitHub Copilot assist with code refactoring tasks?

Answer: A

Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.


NEW QUESTION # 117
What is zero-shot prompting?

Answer: C

Explanation:
Zero-shot prompting involves asking GitHub Copilot a question or giving a task without providing any examples. This relies on the model's pre-existing knowledge to generate a response.


NEW QUESTION # 118
Select a strategy to increase the performance of GitHub Copilot Chat.

Answer: B

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 # 119
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