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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
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 Copilot’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	Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilot’s test suggestions and security optimizations. It covers identification of security vulnerabilities, performance enhancements, and privacy features like content exclusions at repository and organization levels with explanation of their limitations. Candidates learn about safeguarding mechanisms such as duplication detection, contractual protections, security checks, and troubleshooting guide for common Copilot issues including context exclusions and suggestion gaps.
Topic 3	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 4	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.
Topic 5	Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.

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Microsoft GitHub Copilot Sample Questions (Q16-Q21):

NEW QUESTION # 16
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?

A. Reviewing and validating the generated output for accuracy and completeness.
B. The explanations are primarily derived from user-provided documentation.
C. The explanations are dynamically updated based on user feedback.
D. GitHub Copilot Chat uses a static database for generating explanations.

Answer: A

Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.

NEW QUESTION # 17
When can GitHub Copilot still use content that was excluded using content exclusion?

A. If the content exclusion was configured at the enterprise level, and is overwritten at the organization level.
B. When the user prompts with @workspace.
C. When the repository level settings allow overrides by the user.
D. If the contents of an excluded file are referenced in code that is not excluded, for example function calls.

Answer: D

Explanation:
GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.

NEW QUESTION # 18
What are the effects of content exclusions? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot suggestions are no longer available in the excluded files.
B. The IDE will not count coding suggestions in the excluded content.
C. The excluded content is no longer used while debugging the code.
D. The excluded content is not directly available to GitHub Copilot to use as context.

Answer: A,D

Explanation:
Content exclusions prevent GitHub Copilot from using the excluded content as context and stop suggestions from being generated in those files.

NEW QUESTION # 19
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.

NEW QUESTION # 20
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

A. The Gen AI tool is overloaded with too many requests at once.
B. The training data might contain biases or inconsistencies.
C. The Gen AI tool is experiencing downtime and is not fully recovered.
D. The Gen AI tool is programmed with a focus on creativity over factual accuracy.

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.

NEW QUESTION # 21
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