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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	GitHub Copilot
Exam Number:	GH-300
Real Exam Qty:	65
Exam Duration:	100 minutes
Available Languages:	Japanese, Spanish, English, Korean, Portuguese (Brazil)
Passing Score:	700 / 1000
Certificate Validity Period:	1 year (renewable)
Exam Format:	Scenario-based, Multiple-choice
Related Certifications:	GitHub Foundations GitHub Actions
Exam Price:	$99 USD
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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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	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 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.

Microsoft GitHub Copilot Sample Questions (Q121-Q126):

NEW QUESTION # 121
How can GitHub Copilot assist in maintaining consistency across your tests?

A. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.
B. By automatically fixing all tests in the code based on the context.
C. By writing the implementation code for the function based on context.
D. By providing documentation references based on industry best practices.

Answer: A

Explanation:
"Copilot learns from the patterns in your existing tests and suggests similar structures, which helps maintain consistency across the test suite." This confirms that Copilot supports test consistency through pattern recognition and suggestion.
References: GitHub Copilot testing documentation.

NEW QUESTION # 122
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?

A. GitHub Copilot Individual
B. GitHub Copilot Teams
C. GitHub Copilot Business for non-GHE Customers
D. GitHub Copilot Enterprise
E. GitHub Copilot Business

Answer: A

Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.

NEW QUESTION # 123
You are deploying a new development environment to a Windows 11 device.
You need to install GitHub Copilot CLI, and then start an interactive GitHub Copilot CLI session.
Which command should you run for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 124
What is a limitation of content exclusions?

A. Content exclusions are only available in the GitHub Copilot Individual plan.
B. Content exclusions can only be configured by an enterprise administrator.
C. Repository administrators and organization owners cannot manage content exclusion settings.
D. Content exclusions can be worked around as it is only available for Git repositories.

Answer: B

Explanation:
"Content exclusions can only be configured at the enterprise level by an enterprise administrator. Repository or organization admins cannot configure them directly." This confirms that configuration is limited to enterprise administrators, making option C correct.
References: GitHub Copilot content exclusions configuration documentation.

NEW QUESTION # 125
What is few-shot prompting?

A. Telling GitHub Copilot from which sources it should base the response on
B. Telling GitHub Copilot to try multiple times to answer the prompt
C. Telling GitHub Copilot to iterate several times on the answer before returning it to you
D. Telling GitHub Copilot about the mechanism you want it to use and how to incorporate that into the response

Answer: D

Explanation:
"Few-shot prompting is a technique where you provide examples of the mechanism or output you want the model to follow. These examples guide the AI's responses." This proves that option A is correct.
References: GitHub Copilot prompt engineering documentation.

NEW QUESTION # 126
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