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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Exam Duration:100 minutes
Certificate Validity Period:1 year
Exam Format:Multiple-choice, Scenario-based, Proctored
Available Languages:German, Portuguese (Brazil), English, Chinese (Simplified), Arabic (Saudi Arabia), Japanese, Spanish, Korean, French
Real Exam Qty:60–65
Passing Score:700 / 1000
Exam Price:$99 USD
Recommended Training:Microsoft Learn: GitHub Copilot Learning Path
Exam Registration:Microsoft Learn Exam Details
Pearson VUE Registration
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Basic familiarity with GitHub, software development workflows, and at least one programming language; no formal prerequisites required
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/GH-300

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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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.
Topic 5
  • 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 6
  • 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 (Q73-Q78):

NEW QUESTION # 73
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?

Answer: D

Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.
Reference: GitHub Copilot documentation on IP protection and code filtering.


NEW QUESTION # 74
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

Answer: A

Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.
Reference: GitHub Copilot Business audit logs.


NEW QUESTION # 75
What method can be used to interact with GitHub Copilot?

Answer: C

Explanation:
GitHub Copilot is an AI-powered code completion tool that integrates directly into supported Integrated Development Environments (IDEs) and code editors, such as Visual Studio Code, JetBrains IDEs, NeoVim, and others. Developers interact with it through their coding environment, where it provides real-time code suggestions, autocompletions, and (in some cases) chat-like capabilities via extensions or plugins (e.g., GitHub Copilot Chat in supported editors).
Evaluation of Options:
* A. By using a properly configured GitHub CLIThe GitHub CLI (Command Line Interface) is a tool for interacting with GitHub repositories and workflows from the terminal, but it is not a method for interacting with GitHub Copilot. Copilot operates within code editors/IDEs, not through the CLI.
Incorrect.
* B. By using chat capabilities in NeoVimThis is partially correct. GitHub Copilot can be used in NeoVim with the appropriate plugin (e.g., the Copilot.vim plugin), and GitHub Copilot Chat-a feature that allows conversational interaction-may also be available depending on the setup and version.
However, "chat capabilities in NeoVim" alone is not the primary or standard way to describe Copilot interaction, as it's more about code suggestions than chat. This is the closest option but not perfectly precise.Partially correct.
* C. From a watch window in an IDE debug sessionThe "watch window" in an IDE is used during debugging to monitor variable values, not to interact with GitHub Copilot. Copilot provides suggestions while coding, not specifically in debug sessions or watch windows.Incorrect.
* D. From a web browser athttps://github.copilot.comThere is no such URL as "https://github.copilot.
com" dedicated to interacting with GitHub Copilot. Copilot is accessed via GitHub's authentication and integrated into editors/IDEs, not through a standalone web browser interface. Information about Copilot is available on GitHub's official site (e.g.,https://github.com/features/copilot), but interaction happens in the coding environment.Incorrect.
Reference: GitHub Copilot interaction documentation.


NEW QUESTION # 76
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.)

Answer: A,C

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.


NEW QUESTION # 77
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.


NEW QUESTION # 78
......

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