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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft / GitHub
Exam Name:	GitHub Copilot Exam (GH-300)
Exam Number:	GH-300
Exam Duration:	100-120
Real Exam Qty:	60-75 (approximately 65 scored + unscored items)
Exam Price:	USD 99
Related Certifications:	GitHub Copilot Fundamentals Part 2 GitHub Copilot Fundamentals Part 1
Certificate Validity Period:	2 years
Exam Format:	Drag and drop, Multiple choice, Scenario-based questions, Multiple response
Available Languages:	Japanese, Portuguese (Brazil), Korean, Spanish, English
Passing Score:	700/1000
Recommended Training:	GitHub Copilot Fundamentals Part 2 GitHub Copilot Fundamentals Part 1
Exam Registration:	Microsoft Learn Certification Page Pearson VUE Scheduling
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:

Topic	Details
Topic 1	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 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	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	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.

Microsoft GitHub Copilot Sample Questions (Q43-Q48):

NEW QUESTION # 43
How long does it take content exclusion to add or be updated?

A. 60 - 90 minutes
B. 24 hours
C. 45 - 60 minutes
D. Up to 30 minutes

Answer: D

Explanation:
Content exclusions typically take up to 30 minutes to be added or updated.
Reference: GitHub Copilot content exclusion documentation.

NEW QUESTION # 44
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter? (Each correct answer presents part of the solution. Choose two.)

A. Code that contains logical errors or produces unexpected results
B. Code comments containing strong opinions or criticisms
C. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
D. Sexually suggestive or explicit content

Answer: C,D

Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate content. This filter flags prompts or code snippets that contain hate speech, discriminatory language, or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.
Reference: GitHub Copilot documentation on safety and content filtering.

NEW QUESTION # 45
What is used by GitHub Copilot in the IDE to determine the prompt context?

A. The open tabs in the IDE and the current folder of the terminal.
B. Information from the IDE like open tabs, cursor location, selected code.
C. All the code visible in the current IDE.
D. All the code in the current repository and any git submodules.

Answer: B

Explanation:
"GitHub Copilot generates suggestions based on context from the IDE, including the file you are editing, open tabs, cursor position, and any selected code." This proves that the context is drawn from editor state information, making option A correct.
References: GitHub Copilot context and suggestions documentation.

NEW QUESTION # 46
How can GitHub Copilot aid developers in writing documentation for their code?

A. GitHub Copilot cannot assist in writing documentation or comments.
B. GitHub Copilot can only generate content in markdown format.
C. GitHub Copilot can suggest summaries or descriptions based on the code's functionality.
D. GitHub Copilot can automatically generate complete and detailed documentation.

Answer: C

Explanation:
"Copilot can suggest comments and documentation summaries that describe the functionality of the code being written." This makes option C correct, as Copilot provides summaries or descriptions rather than full automatic documentation.
References: GitHub Copilot documentation features.

NEW QUESTION # 47
How does GitHub Copilot Chat ensure that a function works correctly?

A. By automatically writing all the tests for the function.
B. By executing the test cases to validate the correctness of the code.
C. By writing the implementation code for the function.
D. By suggesting assertions based on the code's context and semantics.

Answer: D

Explanation:
GitHub Copilot Chat can suggest assertions based on the code's context and semantics to help developers verify the correctness of their functions. These assertions serve as checks that the function behaves as expected under various conditions.
Reference: GitHub Copilot documentation on testing and code verification.

NEW QUESTION # 48
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