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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:GitHub Copilot Exam (GH-300)
Exam Number:GH-300
Passing Score:700/1000
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Exam Format:Multiple choice, Drag and drop, Multiple response, Scenario-based questions
Exam Price:USD 99
Exam Duration:100-120
Available Languages:Spanish, Japanese, Portuguese (Brazil), English, Korean
Related Certifications:GitHub Copilot Fundamentals Part 2
GitHub Copilot Fundamentals Part 1
Certificate Validity Period:2 years
Recommended Training:GitHub Copilot Fundamentals Part 1
GitHub Copilot Fundamentals Part 2
Exam Registration:Pearson VUE Scheduling
Microsoft Learn Certification Page
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored (Pearson VUE) or test center
Pre Condition:Basic GitHub knowledge and experience using at least one programming language; familiarity with GitHub Copilot recommended but not strictly required
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot/

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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
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
  • 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 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 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 5
  • 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.

Microsoft GitHub Copilot Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.
Reference: GitHub Copilot prompt engineering best practices.


NEW QUESTION # 19
How do you generate code suggestions with GitHub Copilot in the CLI?

Answer: A

Explanation:
"To generate a suggestion, run gh copilot suggest, provide your command description, then review and select from the suggestions returned." This confirms the CLI command flow in option C.
References: GitHub Copilot CLI documentation.


NEW QUESTION # 20
A social media manager wants to use AI to filter content. How can they promote transparency in the platform' s AI operations?

Answer: C

Explanation:
Exact extracts:
* "Transparency. AI systems should be understandable."
References: Microsoft Responsible AI guidelines and transparency notes.


NEW QUESTION # 21
Which Copilot Enterprise features are available in all commercially supported IDEs?

Answer: A

Explanation:
"Copilot Enterprise provides inline code suggestions and Copilot Chat in all commercially supported IDEs.
Additional features, such as pull request summaries and knowledge bases, are available in GitHub.com." This confirms that inline suggestions and Chat are the features consistently available in IDEs.
References: GitHub Copilot Enterprise feature documentation.


NEW QUESTION # 22
Which of the following is a risk associated with using AI?

Answer: B

Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.
Reference: AI risk management documentation.


NEW QUESTION # 23
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