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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
Exam Format:Multiple response, Multiple choice, Scenario-based questions, Drag and drop
Certificate Validity Period:2 years
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Exam Duration:100-120
Related Certifications:GitHub Copilot Fundamentals Part 1
GitHub Copilot Fundamentals Part 2
Available Languages:Japanese, Portuguese (Brazil), Spanish, Korean, English
Exam Price:USD 99
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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Online GH-300 Training Materials - Test GH-300 Guide

The Microsoft GH-300 practice exam software will provide you with feedback on your performance. The Microsoft GH-300 practice test software also includes a built-in timer and score tracker so students can monitor their progress. GH-300 Practice Exam enables applicants to practice time management, answer strategies, and all other elements of the final Microsoft GH-300 certification exam and can check their scores.

Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 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.
Topic 5
  • 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.

Microsoft GitHub Copilot Sample Questions (Q99-Q104):

NEW QUESTION # 99
You have a GitHub Copilot subscription.
You need to start an interactive GitHub Copilot CLI session from the root folder of a project.
If you are NOT authenticated, GitHub Copilot must prompt you to sign in.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 100
Which of the following is correct about GitHub Copilot Knowledge Bases?

Answer: C

Explanation:
GitHub Copilot Knowledge Bases is an Enterprise feature that allows the tool to use an organization's internal documentation and code for more accurate and relevant suggestions.


NEW QUESTION # 101
Why is it important to ensure the security of the code used in Generative AI (Gen AI) tools?

Answer: A

Explanation:
"Securing the code used with generative AI prevents unauthorized access, helps safeguard sensitive data, and protects against potential breaches." This confirms option A is correct because the primary importance lies in avoiding unauthorized access and breaches.
References: GitHub Copilot responsible use and AI security documentation.


NEW QUESTION # 102
Which scenarios can GitHub Copilot Chat be used to increase productivity? (Each correct answer presents part of the solution. Choose two.)

Answer: B,D

Explanation:
Exact extracts:
* "Copilot Chat can generate documentation based on your code."
References: GitHub Copilot Chat productivity documentation.


NEW QUESTION # 103
You have a GitHub repository named Repo1 that has GitHub Copilot enabled.
The developers at your company use Repo1 in Microsoft Visual Studio Code.
You need to identify potentially unoriginal suggestions and prevent secrets from being committed to Repo1.
The solution must ensure that the developers can use inline GitHub Copilot suggestions in Visual Studio Code.
Which two actions should you perform? Each correct answer presents part of the solution.

Answer: B,D

Explanation:
Two separate GitHub features satisfy the security requirements. Push protection is part of secret scanning and blocks supported secrets before they are pushed to a repository, helping prevent credentials from being committed. GitHub Copilot code referencing addresses potentially unoriginal suggestions: when public-code matches are allowed, accepted inline suggestions that match public GitHub code can be logged with source URLs and license information so developers can review the origin. Disabling inline suggestions would violate the requirement that developers continue using Copilot completions. Repository custom instructions may influence generated code, but they do not replace secret scanning or public-code matching. Therefore, enabling push protection together with Copilot code referencing is the correct combination.


NEW QUESTION # 104
......

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