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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Exam Duration:100 minutes
Passing Score:700 / 1000
Available Languages:Chinese (Simplified), German, Japanese, Spanish, English, French, Korean, Portuguese (Brazil), Arabic (Saudi Arabia)
Certificate Validity Period:1 year
Exam Format:Scenario-based, Multiple-choice, Proctored
Real Exam Qty:60–65
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
  • 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
  • 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
  • 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 (Q35-Q40):

NEW QUESTION # 35
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

Answer: B

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.
Reference: GitHub Copilot data analysis limitations.


NEW QUESTION # 36
How long does it take content exclusion to add or be updated?

Answer: D

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


NEW QUESTION # 37
What GitHub Copilot pricing plan gives you access to your company's knowledge bases?

Answer: B

Explanation:
GitHub Copilot Enterprise provides access to your company's knowledge bases, enabling the tool to provide contextually relevant suggestions based on your organization's specific documentation and code.
Reference: GitHub Copilot Enterprise pricing and features.


NEW QUESTION # 38
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

Answer: A,B

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.


NEW QUESTION # 39
How does GitHub Copilot utilize chat history to enhance its code completion capabilities?

Answer: D

Explanation:
"GitHub Copilot Chat can reference the ongoing conversation history to analyze patterns, common coding styles, and errors, providing more accurate and contextual suggestions." This demonstrates that chat history is used to identify patterns and enhance contextual accuracy, making option C correct.
References: GitHub Copilot Chat documentation.


NEW QUESTION # 40
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