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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Responsible :This section of the exam measures skills of AI Ethics Officers and Risk Managers and covers the responsible and ethical usage of AI technologies. It explains the risks and limitations associated with generative AI tools, including biases in training data and the need to validate AI outputs. Candidates learn how to operate AI responsibly by identifying potential harms such as bias, fairness, privacy concerns, and mitigating these harms by applying ethical AI principles.
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.

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Microsoft GitHub Copilot Sample Questions (Q78-Q83):

NEW QUESTION # 78
What are the potential risks associated with relying heavily on code generated from GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot's suggestions may not always reflect best practices or the latest coding standards.
B. GitHub Copilot may increase development lead time by providing irrelevant suggestions.
C. GitHub Copilot may decrease developer velocity by requiring too much time in prompt engineering.
D. GitHub Copilot may introduce security vulnerabilities by suggesting code with known exploits.

Answer: A,D

Explanation:
Heavy reliance on GitHub Copilot can introduce security vulnerabilities if the generated code contains known exploits. Additionally, Copilot's suggestions may not always align with best practices or the latest standards, requiring careful review and validation.
Reference: GitHub Copilot best practices and risk management.

NEW QUESTION # 79
What is the correct way to access the audit log events for GitHub Copilot Business?

A. Use the Audit log section in the organization's GitHub settings
B. Use the Code tab in the GitHub repository
C. Navigate to the Security tab in the organization's GitHub settings
D. Navigate to the Insights tab in the repository settings

Answer: A

Explanation:
Audit log events for GitHub Copilot Business can be accessed through the Audit log section within the organization's GitHub settings. This log provides a record of activities related to Copilot usage and configuration.

NEW QUESTION # 80
When crafting prompts for GitHub Copilot, what is a recommended strategy to enhance the relevance of the generated code?

A. Keep the prompt as short as possible, using single words or brief phrases.
B. Write the prompt in natural language without any programming language.
C. Provide examples of expected input and output within the prompt.
D. Avoid mentioning the programming language to allow for more flexible suggestions.

Answer: C

Explanation:
"To get the best results from GitHub Copilot, provide clear prompts and, when possible, include examples of expected input and output." This establishes that including examples is a recommended prompt engineering strategy.
References: GitHub Copilot prompt engineering documentation.

NEW QUESTION # 81
How does GitHub Copilot identify matching code and ensure that public code is appropriately handled or blocked? (Each correct answer presents part of the solution. Choose two.)

A. Using machine learning models trained only on private repositories
B. Filtering out suggestions that match code from public repositories
C. Implementing safeguards to detect and avoid suggesting verbatim snippets from public code
D. Reviewing and storing user-specific private repository data for future suggestions

Answer: B,C

Explanation:
Exact extracts:
* "GitHub Copilot has a filter to detect code suggestions that match public code on GitHub. When this filter is enabled, Copilot checks suggestions with about 150 characters or more against public code and blocks them if a match is found." References: GitHub Copilot duplication detection and safeguards documentation.

NEW QUESTION # 82
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

A. Provide all information about the utilized files
B. Provide insight on where to get the content from to get a response
C. Provide specific success criteria
D. Provide links to supporting documentation

Answer: C,D

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.
Reference: GitHub Copilot prompt engineering best practices.

NEW QUESTION # 83
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