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| Section | Weight | Objectives |
|---|---|---|
| Privacy Fundamentals and Context Exclusions | 15% | - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot - Content exclusions at repository and organization level - Safeguards: duplication detector, contractual protection, data collection settings - Effects and limitations of content exclusions - Ownership of AI-generated outputs |
| GitHub Copilot Plans and Features | 31% | - Copilot Chat and code suggestion capabilities - Configuration options and productivity features - IDE integration (VS Code, Visual Studio, JetBrains) - Implementing and managing Copilot plans for an organization - Differences between Copilot Individual, Business, and Enterprise plans |
| How GitHub Copilot Works and Handles Data | 15% | - Privacy and data handling within Copilot - The prompt creation process - How Copilot gathers code context - AI model response generation - Understanding that private repository content is not used to train models |
| Responsible AI with GitHub Copilot | 7% | - Bias and fairness in AI-generated code - Risks and limitations of generative AI tools - Privacy and transparency considerations - Ethical AI principles and responsible use - Validating AI output and mitigation strategies |
| Testing with GitHub Copilot | 9% | - Generating unit tests and integration tests - Identifying edge cases - Editor configuration and org-level suggestion settings - Improving test coverage with AI suggestions |
| Developer Use Cases for AI | 14% | - SDLC support and productivity insights - Learning new languages and frameworks - Documentation generation - Debugging applications - Code generation and refactoring - Automating repetitive development tasks |
| Prompt Engineering and AI Interaction | 9% | - Creating effective prompts - Crafting and refining prompts (prompt engineering) - Improving Copilot responses using context - Refining prompts for better coding results |
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NEW QUESTION # 104
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)
Answer: A,B
Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.
Reference: GitHub Copilot language learning documentation.
NEW QUESTION # 105
You use GitHub Copilot Chat to debug a function.
You discover that GitHub Copilot is using irrelevant chat threads to generate output.
You need to ensure that GitHub Copilot only uses new input to generate output.
What should you do, and how will this affect the prompt process flow?
Answer: D
Explanation:
GitHub Copilot Chat uses conversation history as part of the context for later responses. GitHub ' s prompt- engineering guidance recommends keeping chat history relevant, while GitHub ' s AI-usage guidance specifically advises starting a new conversation when moving to unrelated work because a long thread carries prior history into subsequent requests. In this scenario, irrelevant earlier chat threads are contaminating the context used for debugging. Starting a new Copilot Chat session creates a clean conversational context for the new prompt, so the previous thread is no longer included as model input for that new conversation.
Continuing in the same chat would preserve the unwanted context. Therefore, the correct action is to start a new chat, with the effect of removing the prior chat history from the new model input.
NEW QUESTION # 106
Identify the steps involved in the life cycle of a GitHub Copilot code suggestion? (Each correct answer presents part of the solution. Choose two.)
Answer: B,C
Explanation:
Exact extracts:
* "Copilot first captures the context of the developer's code and environment." References: GitHub Copilot technical overview documentation.
NEW QUESTION # 107
How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?
Answer: D
Explanation:
"GitHub Copilot may sometimes suggest deprecated code, APIs, or patterns if these appear in its training data. Users are responsible for reviewing and updating the suggestions." This confirms that Copilot does not automatically update or reject deprecated features, but may still suggest them if they were part of training.
References: GitHub Copilot usage limitations documentation.
NEW QUESTION # 108
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
Answer: A
Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.
NEW QUESTION # 109
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