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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Passing Score:700 / 1000
Certificate Validity Period:1 year
Exam Duration:100 minutes
Exam Format:Proctored, Scenario-based, Multiple-choice
Available Languages:Korean, English, Japanese, German, Spanish, Arabic (Saudi Arabia), French, Chinese (Simplified), Portuguese (Brazil)
Real Exam Qty:60–65
Exam Price:$99 USD
Recommended Training:Microsoft Learn: GitHub Copilot Learning Path
Exam Registration:Pearson VUE Registration
Microsoft Learn Exam Details
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
  • 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 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
  • 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.
Topic 6
  • 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.

Microsoft GitHub Copilot Sample Questions (Q33-Q38):

NEW QUESTION # 33
What role does chat history play in GitHub Copilot's code suggestions?

Answer: D

Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.
Reference: GitHub Copilot Chat history documentation.


NEW QUESTION # 34
What is the best way to share feedback about GitHub Copilot Chat when using it on GitHub Mobile?

Answer: C

Explanation:
The best way to share feedback on GitHub Mobile is using the emojis in the Copilot Chat interface, which allows for direct and immediate feedback.
Reference: GitHub Copilot Mobile documentation.


NEW QUESTION # 35
How can you get multiple suggestions from GitHub Copilot?

Answer: D

Explanation:
You can get multiple suggestions by opening the completions panel in your editor, which displays alternative code suggestions.


NEW QUESTION # 36
What is the correct way to exclude specific files from being used by GitHub Copilot Business during code suggestions?

Answer: D

Explanation:
The correct way to exclude files is through the GitHub Copilot settings in the user interface, which allows administrators to specify files and directories to be ignored.


NEW QUESTION # 37
Which Microsoft ethical AI principle is aimed at ensuring AI systems treat all people equally?

Answer: D

Explanation:
"Fairness. AI systems should treat all people fairly." This principle directly defines fairness as the standard for ensuring equality across all individuals.
References: Microsoft Responsible AI principles.


NEW QUESTION # 38
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