GH-300 Latest Exam Dumps | GH-300 Exam Topics

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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Related Certifications:GitHub Foundations
GitHub Actions
Passing Score:700 / 1000
Real Exam Qty:65
Certificate Validity Period:1 year (renewable)
Exam Duration:100 minutes
Exam Price:$99 USD
Available Languages:Korean, Spanish, Portuguese (Brazil), Japanese, English
Exam Format:Scenario-based, Multiple-choice
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

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GH-300 Exam Topics | GH-300 Exam Fees

The Microsoft GH-300 desktop practice exam software is customizable and suits the learning needs of candidates. A free demo of the GitHub Copilot (GH-300) desktop software is available for sampling purposes. You can change Microsoft GH-300 Practice Exam's conditions such as duration and the number of questions. This simulator creates a GitHub Copilot (GH-300) real exam environment that helps you to get familiar with the original test.

Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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
  • 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.

Microsoft GitHub Copilot Sample Questions (Q111-Q116):

NEW QUESTION # 111
What two options navigate to configure duplicate detection? (Each correct answer presents part of the solution. Choose two.)

Answer: A,C

Explanation:
"Duplicate detection and blocking of public code matches can be configured by enterprise administrators in enterprise or organization-level Copilot policies." This confirms that enterprise and organization settings are the correct paths.
References: GitHub Copilot policy configuration documentation.


NEW QUESTION # 112
How can the insights gained from the metrics API be used to improve the development process in conjunction with GitHub Copilot?

Answer: B

Explanation:
"The Copilot metrics API provides insights into how suggestions are being accepted and used, enabling teams to analyze the relative impact of Copilot-generated code versus manually written code." This confirms option D is correct, as the API enables analysis of Copilot versus manual coding.
References: GitHub Copilot metrics API documentation.


NEW QUESTION # 113
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?

Answer: A

Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.


NEW QUESTION # 114
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

Answer: A,C

Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting "Refactor using GitHub Copilot" from the context menu.
Reference: GitHub Copilot refactoring documentation.


NEW QUESTION # 115
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

Answer: A,C

Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting " Refactor using GitHub Copilot " from the context menu.
Reference: GitHub Copilot refactoring documentation.


NEW QUESTION # 116
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