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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:	GitHub (Microsoft)
Exam Name:	GitHub Copilot Certification Exam
Exam Number:	GH-300
Available Languages:	Portuguese (Brazil), English, Korean, Japanese, Spanish
Exam Format:	Multiple-choice, Proctored (Pearson VUE), Scenario-based
Related Certifications:	GitHub Advanced Security Certification
Certificate Validity Period:	24 months
Exam Price:	$99 USD
Real Exam Qty:	Approximately 60 scored questions + unscored items
Exam Duration:	100 minutes
Passing Score:	Not officially disclosed (commonly reported ~700/1000)
Recommended Training:	GitHub Docs Copilot Guide GitHub Copilot Learning Path (Microsoft Learn)
Exam Registration:	Official GitHub Copilot Certification Page Microsoft Learn Certifications
Sample Questions:	GitHub GitHub-Copilot Sample Questions
Exam Way:	Online proctored or Pearson VUE test center
Pre Condition:	No formal prerequisites required; recommended familiarity with GitHub and basic programming experience
Official Syllabus URL:	https://learn.github.com/certification/COPILOT

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.
Topic 2	Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.
Topic 3	Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.

GitHub CopilotCertification Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

A. Comparing suggestions against public code using machine learning.
B. Reviewing the user's browsing history to identify public repositories
C. Analyzing the context and structure of the code being written
D. Running code suggestions through filters designed to detect public code

Answer: D

Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.

NEW QUESTION # 129
Which of the following statements best describes the impact of GitHub Copilot on the software development process?

A. It reduces developer overhead by automating testing workflows.
B. It increases productivity by automating repetitive coding tasks.
C. It replaces the need for developers in the software development process.
D. It decreases software vulnerabilities from third party dependencies.

Answer: B

Explanation:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.

NEW QUESTION # 130
Identify the steps involved in the life cycle of a GitHub Copilot code suggestion? Each correct answer presents part of the solution. (Choose two.)

A. Processing telemetry data
B. Capturing the user's context
C. Generate suggestions
D. Retraining the model
E. Storing user data

Answer: B,C

Explanation:
GitHub Copilot begins by capturing your current editing context - such as surrounding code, comments, and cursor position - and then feeds that into its AI model to generate code suggestions tailored to what you're working on.

NEW QUESTION # 131
How long does it take content exclusion to add or be updated?

A. 60 - 90 minutes
B. 24 hours
C. Up to 30 minutes
D. 45 - 60 minutes

Answer: C

NEW QUESTION # 132
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 internally by GitHub for improving the search engine.
C. Prompt data is used to train machine learning models for better code suggestions.
D. Prompt data is stored unencrypted for faster processing.

Answer: A,C

Explanation:
Copilot uses your real‑time input (the prompt and surrounding code) to generate relevant, context
‑aware suggestions on the fly.
Under the Individual plan, unless you opt out, your prompts and accepted suggestions are fed back into GitHub's model‑training pipeline to improve future code completions.

NEW QUESTION # 133
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