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Microsoft GH-300 Exam Overview:

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

Topic	Details
Topic 1	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 2	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 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	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 5	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 6	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.

Microsoft GitHub Copilot Sample Questions (Q16-Q21):

NEW QUESTION # 16
What is few-shot prompting?

A. Telling GitHub Copilot to try multiple times to answer the prompt
B. Telling GitHub Copilot from which sources it should base the response on
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 # 17
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

A. Free (not billed) for all open source projects
B. Monthly, as a metered service based on actual consumption
C. Monthly as a subscription
D. Annually as a subscription

Answer: C,D

Explanation:
GitHub Copilot Individual is billed as a monthly or annual subscription.

NEW QUESTION # 18
What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

A. Clearly defining the problem or task
B. Use a .gitignore file to exclude irrelevant files
C. Using meaningful variable names
D. Including personal information in the code comments
E. Providing examples of desired output

Answer: A,C,E

Explanation:
The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.

NEW QUESTION # 19
How can GitHub Copilot assist with code refactoring tasks?

A. GitHub Copilot can fix syntax errors without user input.
B. GitHub Copilot can suggest refactoring improvements for better code quality.
C. GitHub Copilot can remove unnecessary files from the project directory.
D. GitHub Copilot can automatically rewrite code to follow best practices.

Answer: B

Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.

NEW QUESTION # 20
Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data?
(Each correct answer presents part of the solution. Choose two.)

A. Real-time user input helps generate context-aware code suggestions.
B. Prompt data is used to train machine learning models for better code suggestions.
C. Prompt data is stored unencrypted for faster processing.
D. Prompt data is used internally by GitHub for improving the search engine.

Answer: A,B

Explanation:
Exact extracts:
* "For GitHub Copilot Individual, prompts and code completions may be retained and used to train machine learning models to improve Copilot." References: GitHub Copilot data usage and privacy documentation.

NEW QUESTION # 21
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