GitHub GH-300 Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for GitHub Copilot (GH-300) topics such as prompt context, chat workflows, generated-code review, testing, enterprise policy, data protection, exclusions, and responsible AI-assisted development. The prompts focus on how a developer or administrator should use Copilot safely inside a real software workflow.

Where these questions fit in the GH-300 guide

The sample set below is part of the GitHub Copilot GH-300 guide path:

• GitHub certification hub
• GH-300 exam guide
• GH-300 cheat sheet
• GH-300 study plan
• GH-300 FAQ
• GH-300 resources
• GH-300 glossary

GH-300 Copilot sample questions

Work through each prompt before opening the explanation. Strong answers treat Copilot as an assistant that needs context, validation, policy, and human accountability.

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Question 1

Topic: Prompting with useful context

A developer asks Copilot to write a data-validation function. The first suggestion ignores project conventions and edge cases. What is the best next step?

• A. Accept the first suggestion because AI-generated code is automatically aligned to local conventions.
• B. Provide relevant file context, expected inputs, edge cases, style constraints, and test expectations before asking for a revised implementation.
• C. Paste production secrets into the prompt so Copilot can test the function against real systems.
• D. Disable code review because Copilot has already reviewed the implementation.

Best answer: B

Explanation: Copilot output improves when the prompt includes useful context and constraints. The developer still needs to validate the result through tests and review.

Why the other choices are weaker:

• A treats the suggestion as authoritative without checking fit.
• C risks exposing sensitive data and is not needed for a coding prompt.
• D removes human accountability and normal engineering controls.

What this tests: Prompt context, generated-code review, secure prompting, and developer responsibility.

Related topics: Prompting; Context; Code review; Testing

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Question 2

Topic: Validating generated code

Copilot suggests an authentication helper that appears to work in a demo but disables certificate validation to simplify an HTTP request. What should the developer do?

• A. Accept the suggestion because generated code is usually safe if it compiles.
• B. Hide the helper in a utility file so reviewers are less likely to question it.
• C. Reject or revise the suggestion, preserve certificate validation, and add tests or review notes for the security-sensitive behavior.
• D. Use the suggestion only in production because demos are less important.

Best answer: C

Explanation: Copilot suggestions must be reviewed like any other code. Disabling certificate validation is a security-sensitive behavior, so the developer should not accept it just because the code compiles or passes a trivial demo.

Why the other choices are weaker:

• A confuses compilation with security and correctness.
• B intentionally weakens review transparency.
• D reverses risk priorities by putting unsafe code into the highest-impact environment.

What this tests: Human review of generated code, secure coding judgment, and testing expectations.

Related topics: Generated code; Security review; Testing; Human accountability

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Question 3

Topic: Enterprise policy for sensitive repositories

An enterprise enables Copilot for most teams but has a repository containing regulated customer data and proprietary algorithms. The security team wants to reduce the chance that sensitive context is used in AI assistance. What is the best administrative response?

• A. Tell developers to remember not to open sensitive files but leave all policy settings unchanged.
• B. Copy the sensitive files into prompts so Copilot can learn which content to avoid later.
• C. Disable all repository permissions because Copilot policy cannot coexist with normal GitHub access.
• D. Apply the appropriate organization or enterprise Copilot policies and repository exclusions for sensitive code, then document acceptable-use expectations.

Best answer: D

Explanation: Sensitive repositories need policy-level controls, not only reminders. The stronger answer combines administrative settings with clear user guidance.

Why the other choices are weaker:

• A relies on memory instead of enforceable governance.
• B deliberately exposes the sensitive material in the AI workflow.
• C confuses repository authorization with Copilot enablement and exclusions.

What this tests: Enterprise Copilot governance, repository exclusions, sensitive data handling, and acceptable-use policy.

Related topics: Enterprise policy; Repository exclusions; Sensitive data; Governance

Independent study note

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