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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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
  • How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.
Topic 4
  • 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 5
  • 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 6
  • 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.

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Microsoft GitHub Copilot Sample Questions (Q62-Q67):

NEW QUESTION # 62
How does GitHub Copilot Chat help in understanding the existing codebase?

Answer: B

Explanation:
GitHub Copilot Chat helps in understanding existing codebases by answering questions about the code and generating explanations. This allows developers to quickly grasp the functionality and structure of unfamiliar code.
Reference: GitHub Copilot Chat documentation on code understanding.


NEW QUESTION # 63
A company is currently storing code in Bitbucket and would like to use GitHub Copilot. Which GitHub Copilot plan will be most cost effective to allow them to manage users with their Identity Provider (e.g. Okta)?

Answer: D

Explanation:
GitHub Copilot Enterprise is the most cost-effective plan for managing users with an Identity Provider like Okta, as it provides enterprise-level features and integration.
Reference: GitHub Copilot pricing and features.


NEW QUESTION # 64
Why is code reviewing still necessary when using GitHub Copilot to write tests?

Answer: D

Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.
Reference: GitHub Copilot testing best practices.


NEW QUESTION # 65
Why is code reviewing still necessary when using GitHub Copilot to write tests?

Answer: D

Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.


NEW QUESTION # 66
What kind of insights can the GitHub Copilot usage metrics API provide to help evaluate the effectiveness of GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
Exact extracts:
* "The Copilot usage metrics API allows organizations to track the number of suggestions shown and accepted by members." References: GitHub Copilot metrics API documentation.


NEW QUESTION # 67
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