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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Domain 4: Prompt Crafting and Prompt Engineering This section measures skills of Software Developers and AI Interaction Designers in effectively crafting prompts to optimize Copilotโ€™s output. It reviews foundational concepts such as prompt components, the role of language in prompting, zero-shot vs. few-shot prompting, and how chat history influences responses. Best practices and engineering principles for prompt design and training methods are also covered.
Topic 2
  • GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
Topic 3
  • Domain 6: Testing with GitHub Copilot: This section measures abilities of QA Engineers and Test Automation Specialists to use Copilot for test generation, including unit and integration tests. It explains how Copilot can identify edge cases and assist in writing assertions. The domain also covers different Copilot subscription SKUs, privacy considerations, organizational code suggestion settings, and configuration files related to Copilot.

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Microsoft GitHub Copilot Sample Questions (Q113-Q118):

NEW QUESTION # 113
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution.
Choose two.)

Answer: C,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 # 114
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 # 115
When using GitHub Copilot Chat to generate boilerplate code for various test types, how can you guide the AI to follow the testing standards of your company?

Answer: D

Explanation:
"The quality of Copilot Chat responses depends on the clarity and specificity of your prompts. You can guide Copilot to follow organizational standards by including examples or requirements in your request." This means prompt engineering with specific examples is the way to align with company standards.
References: GitHub Copilot Chat best practices documentation.


NEW QUESTION # 116
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

Answer: B

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.
Reference: Generative AI limitations documentation.


NEW QUESTION # 117
What caution should developers exercise when using GitHub Copilot for assistance with mathematical computations?

Answer: D

Explanation:
"Copilot's responses are generated based on patterns in training data and are not guaranteed to be mathematically accurate. Developers must verify calculations independently." This shows that Copilot cannot guarantee correctness in math and relies on patterns, making option C correct.
References: GitHub Copilot responsible use documentation.


NEW QUESTION # 118
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