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Microsoft GH-300 Exam Syllabus Topics:

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

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Microsoft GitHub Copilot Sample Questions (Q115-Q120):

NEW QUESTION # 115
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution.
Choose two.)

Answer: B,D

Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.
Reference: GitHub Copilot prompt engineering and context management.


NEW QUESTION # 116
What is the best way to share feedback about GitHub Copilot Chat when using it on GitHub Mobile?

Answer: A

Explanation:
The best way to share feedback on GitHub Mobile is using the emojis in the Copilot Chat interface, which allows for direct and immediate feedback.
Reference: GitHub Copilot Mobile documentation.


NEW QUESTION # 117
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?

Answer: A

Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.


NEW QUESTION # 118
How does GitHub Copilot Chat ensure that a function works correctly?

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.
Reference: GitHub Copilot documentation on testing and code verification.


NEW QUESTION # 119
Select a strategy to increase the performance of GitHub Copilot Chat.

Answer: C

Explanation:
"Performance of Copilot Chat can be improved with prompt engineering. More specific prompts give more relevant and accurate answers." This confirms that applying prompt engineering techniques is the valid strategy.
References: GitHub Copilot Chat best practices documentation.


NEW QUESTION # 120
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