Free Practice Questions GitHub Copilot GH-300 Exam Preparation | Now 2026

Microsoft GH-300 Practice Test

The Microsoft GH-300 practice test helps you check whether you can use GitHub Copilot effectively, safely, and responsibly in real development situations. You can Browse our complete practice-test library to find focused exam preparation, then use this GH-300 page to understand the current topics, practise decision-making, and organize your study time before taking the GitHub Copilot certification exam.

What is the Microsoft GH-300 exam?

GH-300 is the exam code for the GitHub Copilot certification. It measures whether you can use GitHub Copilot to improve software-development productivity, quality, testing, and security while applying responsible AI practices. The certification is classified as intermediate, so it goes beyond identifying basic features. You should understand when to use a feature, how context affects an answer, why AI-generated code must be validated, and how organizations control privacy and access.

The current official GitHub Copilot certification page recommends experience with GitHub fundamentals and at least one programming language. You do not need to be an expert in many languages, but you should be comfortable reading code, recognizing tests, and working in an editor or terminal.

Microsoft updated the GH-300 certification page on August 7, 2026. This article follows that current outline, including Copilot CLI, agent mode, Copilot Edits, MCP, agent sessions, sub-agents, Spaces, Spark, reusable prompt files, code review, organizational policies, audit logs, and subscription management.

Who should take the GH-300 GitHub Copilot exam?

GH-300 is most useful for people who already understand basic software-development workflows and want to demonstrate practical GitHub Copilot knowledge. Suitable candidates include:

• Students who have used GitHub and written code in at least one language
• Junior and mid-level developers using AI assistance in an IDE
• DevOps learners who want to use Copilot in repositories, terminals, and automation workflows
• App makers who use AI to explain, generate, refactor, document, and test code
• Technology managers who need to understand Copilot policies, safeguards, and organizational controls
• Quality engineers who want to generate test ideas, assertions, sample data, and edge cases
• Security-conscious developers who review AI output for vulnerabilities and privacy risks
• Teams adopting agent mode, Copilot CLI, code review, or reusable instructions

The certification is not ideal as a student’s very first introduction to GitHub or programming. If repositories, commits, branches, pull requests, command-line tools, and unit tests are unfamiliar, learn those basics before focusing on GH-300 scenarios.

Students comparing this exam with other credentials can Browse Microsoft certification practice exams and choose the certification that best matches their current experience.

What does a GH-300 practice test measure?

A useful GH-300 practice test should measure understanding and judgment, not memorization of product slogans. It should check whether you can:

1. Recognize risks and limitations of generative AI.
2. Select an appropriate Copilot experience for a development task.
3. Provide useful instructions and relevant context.
4. Review generated code instead of accepting it automatically.
5. Use Copilot to improve tests, documentation, refactoring, and learning.
6. Explain how data and prompts move through the Copilot system at a high level.
7. Apply content exclusions, public-code filtering, and organizational policies.
8. Troubleshoot why a feature, suggestion, or exclusion is not behaving as expected.

For example, a practice question might ask whether inline suggestions, chat, agent mode, Copilot CLI, or a reusable prompt file is the best option for a given task. Another might ask how to reduce hallucinations, protect sensitive code, create better context, or validate a security-related recommendation.

The goal is not to memorize a single interface because GitHub Copilot changes quickly. Learn the purpose, limitations, controls, and decision-making behind each feature.

Current GH-300 exam domains and weights

The official GH-300 study guide lists the following distinct detailed domains as of August 7, 2026:

Important editorial note: Microsoft’s current “Skills at a glance” list displays the 25–30% Copilot-features line twice. The detailed objectives contain one Copilot-features section, so this page presents the six distinct detailed domains rather than treating the repeated label as a separate topic. Always check the official page in case Microsoft corrects the display or revises the outline.

1. Use GitHub Copilot responsibly: 15–20%

This domain asks whether you understand that an AI assistant can be useful without being automatically correct. Large language models generate likely outputs from patterns and context. They can misunderstand a request, omit requirements, produce insecure code, repeat bias, or return code that looks convincing but fails in real use.

Responsible use means keeping a human involved. Before accepting generated work, you should:

• Read and understand the suggestion.
• Confirm that it satisfies the stated requirement.
• Run appropriate tests and static analysis.
• Check authentication, authorization, input handling, and error handling.
• Look for exposed secrets, unsafe dependencies, and insecure defaults.
• Review licensing or public-code matching information when relevant.
• Follow organizational policies and applicable legal requirements.
• Ask a qualified person to review high-risk changes.

Potential harms can include security vulnerabilities, privacy exposure, unfair or biased behavior, incorrect business logic, intellectual property concerns, and overreliance on automation. Mitigation may involve clearer context, smaller tasks, validation, code review, security scanning, access controls, and refusing to use AI for a task when the risk cannot be managed safely.

GitHub’s responsible-use documentation repeatedly emphasizes understanding each feature’s purpose and limitations. For the exam, choose answers that combine useful AI assistance with human review and established engineering controls.

2. Use GitHub Copilot features: 25–30%

This is the largest distinct domain. You should understand how Copilot appears across IDEs, the terminal, and GitHub, and how different features support different types of work.

Inline suggestions, chat, and agent mode

Inline suggestions appear while you type and can complete a line, block, or pattern based on nearby context. They are useful when the next code is reasonably predictable.

Copilot Chat supports questions and interactive requests. You might ask it to explain code, suggest a refactor, create a test, compare approaches, or help diagnose an error. Good chat use requires a clear goal and relevant context.

Agent mode can plan and complete multi-step tasks, use tools, edit several files, and respond to results. It is more autonomous than a simple suggestion, so students should understand permission boundaries, review points, and the need to inspect changes.

Copilot Edits helps apply coordinated changes across files. It is useful when you want the assistant to modify a defined part of a codebase while you review the proposed edits.

GitHub Copilot CLI

Copilot CLI brings AI assistance to the terminal. The current blueprint expects students to understand its purpose, installation at a high level, core features, interactive sessions, script generation, and file-management use cases. GitHub’s Copilot CLI documentation explains that the tool may read, modify, and execute files within a trusted working folder, which is why permissions and review matter.

Know the difference between asking for an explanation and allowing an agentic session to take action. A safe workflow is to begin in the correct directory, confirm which files are in scope, review proposed commands, limit tools and permissions, inspect the diff, and run tests before keeping the result.

Agents, agent sessions, sub-agents, and MCP

An agent session allows Copilot to work through a larger task while keeping related context together. A sub-agent can handle a focused part of a task so the main agent does not carry every detail in one context. Delegation can improve organization, but the user remains responsible for checking results.

MCP, or Model Context Protocol, lets compatible AI tools connect to approved external tools and data sources. MCP can give an agent more useful context or capabilities, but each connection should be treated as an access decision. Apply least privilege and use only trusted servers and necessary tools.

Code review, Spaces, Spark, and pull-request summaries

Copilot can assist with code review by identifying possible issues and suggesting improvements. It does not replace human review, automated tests, or security analysis.

Copilot Spaces can collect curated context for a task or subject. GitHub explains that Copilot Spaces help ground responses in selected context. This can reduce repeated explanations and help Copilot work with relevant repository files, issues, pull requests, instructions, or added text.

GitHub Spark supports creating and developing applications with AI assistance. At the exam level, understand its broad purpose and when it may be more suitable than a small inline code completion.

Pull-request summaries can help reviewers understand the purpose and scope of changes, but reviewers must still inspect the actual diff. Instruction files can define preferred review standards or development guidance, while reusable prompt files support repeated tasks with a consistent structure.

Organization-wide settings and policies

Administrators can control which Copilot features are available across IDEs and GitHub, enable or restrict code-review capabilities, review relevant audit events, and manage subscriptions through supported APIs. Focus on why centralized policy exists: consistent governance, cost control, privacy, security, and appropriate feature access.

3. Understand GitHub Copilot data and architecture: 10–15%

You do not need to memorize a low-level infrastructure diagram, but you should understand the general suggestion lifecycle.

A user enters a prompt or begins writing code. The client gathers permitted context, such as the current file, selected text, open files, conversation history, or repository instructions. That information is used to construct a prompt. A proxy or service layer can apply policy checks, filtering, and routing before the model processes the request. The returned output may then pass through post-processing and safety filters before being displayed to the user.

The exact context depends on the feature, client, plan, settings, and task. More context is not always better. Relevant, accurate context usually improves an answer, while unrelated material can distract the model or use the available context window.

Also understand common LLM limitations:

• Responses are probabilistic and may differ between attempts.
• A confident tone does not prove correctness.
• Context can be missing, outdated, or misunderstood.
• Long conversations can reduce attention to earlier requirements.
• Generated code may contain logic, security, performance, or compatibility problems.
• Models may struggle with uncommon languages, complex systems, or hidden requirements.

The correct exam mindset is to use Copilot as an assistant within a controlled engineering process, not as an unquestioned authority.

4. Apply prompt engineering and context crafting: 10–15%

An effective prompt usually includes a clear task, relevant context, constraints, and the desired output. Instead of asking “Fix this code,” a stronger prompt might state the error, expected behavior, language version, files in scope, performance or security limits, and the format of the requested answer.

Use this simple prompt structure:

1. Goal: What outcome do you need?
2. Context: What code, error, architecture, or business rule matters?
3. Constraints: What must Copilot preserve or avoid?
4. Acceptance criteria: How will you know the result is correct?
5. Output format: Do you want an explanation, plan, patch, tests, or comparison?

Zero-shot prompting asks for a result without giving an example. Few-shot prompting provides one or more examples to demonstrate the expected pattern. Few-shot prompts are useful when naming, formatting, or logic must follow an established style.

Conversation history can help Copilot retain the direction of a task, but outdated instructions can also cause confusion. Start a new session or restate the requirement when the old context no longer applies. GitHub supports custom instructions for continuing guidance and prompt files for repeatable tasks. The official prompt-file documentation shows examples for tasks such as README creation, API documentation, code review, and unit-test generation.

5. Improve developer productivity with GitHub Copilot: 10–15%

This domain focuses on practical outcomes. Copilot can help developers:

• Generate starter code or repetitive structures.
• Explain unfamiliar code and APIs.
• Refactor code for readability or maintainability.
• Write docstrings, comments, README content, and API documentation.
• Generate sample data for development and testing.
• Modernize legacy patterns while preserving behavior.
• Suggest unit and integration tests.
• Identify possible edge cases and assertions.
• Explore performance improvements.
• Point out possible security concerns.

Productivity does not mean accepting more code in less time. A change is productive only when it is understandable, correct, maintainable, tested, and suitable for the project.

When generating tests, specify the behavior under test, framework, inputs, expected outputs, boundary conditions, and failure cases. Review whether the tests verify real behavior rather than simply matching the generated implementation. For legacy modernization, first establish tests that capture current behavior, make changes in manageable steps, and compare results.

Copilot can also reduce context switching by explaining an error or suggesting a command inside the current tool. However, students should still consult authoritative documentation when accuracy, security, or version compatibility matters.

6. Configure privacy, content exclusions, and safeguards: 10–15%

This domain covers the controls used to limit unwanted access and reduce risky output.

Content exclusion can prevent Copilot from using specified files or paths in supported experiences. It may be appropriate for sensitive files, generated code, vendor content, or material that should not influence suggestions. GitHub’s content-exclusion documentation notes that exclusions affect supported suggestions, chat responses, and code review, but also documents important limitations. Students should not assume that one exclusion setting applies identically to every feature, editor, remote filesystem, or symbolic link.

Public-code matching filters help control how suggestions that match public code are handled. A matching-code notice can provide source and licensing information when the setting allows such suggestions. Regardless of the setting, teams should perform appropriate review, testing, intellectual-property checks, and security scanning.

Privacy and safeguard questions may ask you to choose the most appropriate control or troubleshoot a missing suggestion. Check the assigned Copilot plan, organizational policy, feature availability, editor extension, authentication, content exclusion, repository context, and public-code filtering settings. Use the narrowest change that meets the requirement rather than disabling protections broadly.

Important GH-300 comparisons to remember

How Edurely Builds Your GH-300 Exam Readiness

GH-300 preparation should help you become a better Copilot user, not simply repeat product terms. Our practice approach turns the exam outline into manageable study sessions and gives you a clear next step after every attempt.

When you prepare with us, you receive a learning experience built around:

• Current domain coverage: Questions are organized around the newest published GH-300 objectives.
• Clear explanations: Each answer teaches the decision, not only the correct option.
• Practical scenarios: Prompts reflect realistic development, testing, privacy, and administration situations.
• Topic-level progress checks: Results help you see whether responsible AI, features, architecture, prompting, productivity, or privacy needs more work.
• Student-friendly language: Technical terms are explained without unnecessary complexity.
• Timed mock practice: Later attempts help you practise reading carefully under an exam-style time limit.
• Focused revision: Missed questions become a personal review list instead of a reason to restart everything.

Aim to explain why an option is safe and suitable, not just recognize its name. When you are ready to turn reading into active recall, Visit the Edurely practice-test platform and use your results to plan the next study session.

How to use a GH-300 practice test effectively

Begin with a diagnostic attempt

Take a short mixed quiz before deep study. Record your result for each of the six distinct domains. A low starting score is useful because it shows where your time will have the greatest effect.

Review the current outline

Confirm that your materials include the August 7, 2026 objectives. Older courses may not cover Copilot CLI sessions, sub-agents, MCP, Spaces, Spark, reusable prompt files, or updated organizational controls.

Practise in a safe repository

Use a disposable repository containing non-sensitive sample code. Try inline suggestions, Chat, test generation, explanations, refactoring, and permitted agent features. Inspect every change before accepting it.

Review all guesses and errors

For every uncertain answer, write the tested concept, the clue you missed, and the reason the correct option fits. A correct guess should still enter your review list.

Retest with new scenarios

Do not immediately repeat the same question set. Study the concept, practise it, and then use a fresh scenario. This checks learning rather than short-term screen memory.

Add timed practice near the end

The official exam provides 100 minutes, but the question count can vary. Build steady pacing without assuming an unofficial number of questions.

Four-week GH-300 study plan

Week 1: Responsible AI and core Copilot experiences

Study generative AI limitations, ethical use, potential harm, output validation, inline suggestions, Chat, Copilot Edits, and agent mode. Use a safe sample project to compare how each feature supports a different task.

Weekly goal: Explain why a plausible answer can still be unsafe or incorrect and choose the right Copilot experience in basic scenarios.

Week 2: CLI, agents, context, and prompting

Review Copilot CLI, trusted folders, permissions, sessions, agent delegation, sub-agents, and MCP. Practise prompts with a goal, context, constraints, acceptance criteria, and output format. Compare zero-shot and few-shot prompting.

Weekly goal: Write clear prompts and describe how context and permissions affect agent behavior.

Week 3: Productivity, testing, code review, and organizational features

Use Copilot to explain, document, refactor, and test sample code. Review edge cases, assertions, sample data, security suggestions, performance ideas, Spaces, Spark, pull-request summaries, code review, instruction files, audit events, policies, and subscription management.

Weekly goal: Show that productivity includes verification, maintainability, and governance—not only faster code generation.

Week 4: Data flow, privacy, safeguards, and mock exams

Study prompt construction, data flow, proxy filtering, post-processing, LLM limitations, content exclusion, public-code matching, and troubleshooting. Take at least two fresh timed practice tests on different days. Review every uncertain answer.

Weekly goal: Achieve consistent results across all six distinct domains and explain your reasoning without relying on memorized answer letters.

Hands-on GH-300 practice activities

Use non-sensitive sample code and complete these tasks:

1. Ask Copilot Chat to explain an unfamiliar function.
2. Compare an inline suggestion with a direct chat request.
3. Refactor a small function and inspect the diff.
4. Generate unit tests, then add a missing edge case manually.
5. Ask for a security review and verify each recommendation independently.
6. Create a prompt with clear constraints and acceptance criteria.
7. Create a few-shot prompt using two examples.
8. Add repository custom instructions for style and testing preferences.
9. Create or study a reusable prompt file for a repeated task.
10. Compare a simple chat request with an agent-mode task.
11. Review tool permissions before allowing agentic actions.
12. Try Copilot CLI in a disposable folder and inspect every proposed action.
13. Identify files that an organization might exclude from Copilot context.
14. Review how public-code matching settings affect the response workflow.
15. Summarize what should be checked before accepting AI-generated code.

Common mistakes students make while preparing

Studying the old blueprint

The exam changed on August 7, 2026. Confirm that your material includes the newest CLI, agent, MCP, Spaces, Spark, reusable prompt, policy, audit, and subscription objectives.

Treating Copilot as always correct

GH-300 repeatedly tests responsible use. Syntax that looks valid may still be insecure, inefficient, incompatible, or logically wrong.

Learning product names without use cases

Be able to explain why a developer would choose inline suggestions, Chat, agent mode, Edits, CLI, Spaces, or a prompt file.

Ignoring privacy and administration

Developers often focus only on code generation. The exam also covers data flow, settings, content exclusions, public-code filters, policies, audit logs, and subscription management.

Writing vague prompts

Practise including requirements, context, constraints, acceptance criteria, and desired output. Good prompting is a repeatable engineering skill.

Using unauthorized exam dumps

Question dumps can be inaccurate, outdated, and inconsistent with certification rules. Use current official documentation and original practice scenarios that teach the published objectives.

Repeating the same mock test immediately

An improved score may reflect memory of the question order. Study the missed concept and use a new scenario after a delay.

GH-300 exam-day advice

• Register through the official certification page and confirm your time zone.
• Use a personal Microsoft account when possible. Microsoft warns that exam records tied to a work or school account may be lost if you leave that organization.
• Check Pearson VUE’s current identification, system, room, and check-in requirements.
• Use the official exam sandbox before your appointment.
• Read each scenario for the actual goal: productivity, safety, context, privacy, or administration.
• Prefer options that combine AI assistance with validation and least privilege.
• Do not assume more automation is always the better answer.
• Manage the 100-minute exam period carefully.
• Microsoft states that Microsoft Learn access is not available during GitHub exams in its exam-experience guidance.
• Review the current retake policy before scheduling another attempt. The certification page says the first retake may occur after 24 hours; later waiting periods vary.

Microsoft GH-300 frequently asked questions

What is GH-300?

GH-300 is the exam code for the GitHub Copilot certification. It assesses responsible AI, Copilot features, architecture and data flow, prompt engineering, developer productivity, privacy, content exclusions, and safeguards.

Is GH-300 a Microsoft or GitHub exam?

GH-300 is a GitHub certification listed and scheduled through Microsoft’s credential platform, with exam delivery through Pearson VUE. The product assessed is GitHub Copilot.

Is GH-300 a beginner exam?

No. Microsoft labels GH-300 as intermediate. Candidates should know GitHub fundamentals and have experience with at least one programming language.

Do I need a programming background for GH-300?

Yes, practical experience with one or more programming languages is recommended. You should be able to read generated code, understand tests, identify errors, and judge whether a suggestion meets a requirement.

Are there formal prerequisites for GH-300?

The official GitHub certification information does not list a required prior certification. Working knowledge of GitHub and practical Copilot experience are recommended.

Do I need to pass GH-900 before GH-300?

No formal requirement says you must pass GH-900 first. However, GH-900 or equivalent GitHub fundamentals knowledge can help if repositories, branches, pull requests, and GitHub collaboration are new to you.

How difficult is the GH-300 exam?

The exam can be challenging because it combines feature knowledge with responsible AI judgment, prompt design, data flow, testing, security, privacy, and organizational controls. Hands-on use is more effective than memorizing definitions alone.

How long is the GH-300 exam?

The current certification page gives candidates 100 minutes to complete the assessment. Allow additional appointment time for check-in and instructions.

What is the GH-300 passing score?

The official study guide states that a score of 700 or higher is required. This is a scaled score and does not mean that exactly 70% of questions must be answered correctly.

How many questions are on GH-300?

Microsoft does not publish a fixed GH-300 question count on the official page. Exam forms can change, so study the full outline instead of relying on an unofficial number.

What question formats appear on GH-300?

Microsoft does not guarantee specific formats in advance. The official exam sandbox demonstrates the testing interface and representative interactions, but it does not reveal the real exam questions.

Is GH-300 open book?

No. Microsoft’s exam policy states that in-exam Microsoft Learn access is not available for GitHub exams. Prepare to answer without searching documentation.

Can I take GH-300 online?

The exam is proctored and scheduled through Pearson VUE. The available delivery choices and requirements shown during scheduling can depend on your location and current provider policies.

How much does GH-300 cost?

The official page calculates the price according to the country or region where the exam is proctored. Select your location during official registration for the current price.

Which languages are available?

The official page currently lists English, Spanish, Portuguese (Brazil), Korean, and Japanese. Verify availability again when scheduling.

Is GitHub Copilot CLI on the GH-300 exam?

Yes. The August 2026 outline includes the CLI’s purpose, installation steps at a high level, important features and commands, interactive sessions, script generation, and file management.

Are agent mode and MCP covered?

Yes. The current skills include agent mode, Copilot Edits, MCP, agent sessions, and delegating focused work to sub-agents. Study their purposes, permissions, context, and safeguards.

Does the exam include Copilot Spaces and Spark?

Yes. The updated outline names Spaces and Spark along with pull-request summaries, code review, instruction files, and prompt-file reuse.

Does GH-300 cover administration?

Yes. Candidates should understand organization-wide policies, feature availability, code-review policies, audit events, REST API subscription management, privacy settings, content exclusions, and public-code filtering.

How long should I study for GH-300?

A candidate already using Copilot regularly may need two to three weeks of focused review. A student with limited experience may need four to eight weeks, including safe hands-on practice. Use domain scores to set your timeline.

How many practice tests should I take?

Start with one diagnostic assessment, use topic quizzes during study, and complete at least two fresh timed mock tests near exam day. Understanding explanations is more useful than taking many repeated sets.

Can students receive a GH-300 exam voucher?

At the time of this update, GitHub Learn says verified GitHub Student Developer Pack members may earn a voucher through the DataCamp benefit after completing the required GitHub Foundations learning path. The voucher may be used for GH-900 or GH-300. Offers, eligibility, costs, and delivery times can change, so check the current official terms before beginning the path.

What happens if I fail GH-300?

Use the score report to identify weaker skill areas and complete targeted practice before trying again. The current certification page says a first retake may be scheduled after 24 hours, while later retake waiting periods vary.