GH-300 — GitHub Copilot (GH-300) Exam Quick Reference

Exam focus at a glance

This Quick Reference supports candidates preparing for the GitHub GitHub Copilot (GH-300) exam, code GH-300. Expect scenario questions about using GitHub Copilot effectively, selecting the right Copilot surface, applying responsible AI practices, and administering Copilot in an organization.

Copilot surface selection matrix

Plans and feature distinctions

Feature packaging can change, but GH-300 scenarios usually test the distinction between individually managed Copilot use and organization-managed Copilot use.

Business vs Enterprise exam cues

Core terminology

Prompting quick reference

Strong prompt pattern

Use this structure when asking Copilot Chat for substantial work:

Goal: What you want built, changed, explained, or tested.
Context: Relevant files, selected code, framework, language, versions, constraints.
Requirements: Behavior, edge cases, performance, security, style, compatibility.
Output format: Code only, step-by-step explanation, test cases, checklist, diff-style plan.
Validation: Ask for risks, assumptions, and how to test the result.

Prompt improvement examples

Inline completion prompting

For code completion, comments and naming often matter more than long chat prompts.

## Create a function that validates a password.
## Requirements:
## - at least 12 characters
## - at least one uppercase letter
## - at least one lowercase letter
## - at least one digit
## - at least one symbol
## Return True or False; do not raise exceptions.
def is_valid_password(password: str) -> bool:

Context rules and decision points

High-yield context traps

Admin and governance reference

Governance decision table

Privacy, security, and responsible AI

Privacy and data handling distinctions

Responsible use checklist

Before accepting Copilot output, verify:

• Correctness against requirements, not just syntax.
• Security: authn/authz, validation, injection, error handling, secrets, crypto misuse.
• Licensing and provenance concerns for substantial or matching code.
• Maintainability: readability, project conventions, dependency choices.
• Test coverage: positive, negative, edge, regression, and failure paths.
• Performance and scalability assumptions.
• Accessibility and internationalization where relevant.
• Whether generated comments accurately describe the code.

Security review prompts

Review the selected code for security issues.
Focus on input validation, authorization, injection, secret exposure,
error handling, insecure dependencies, and unsafe defaults.
Return findings with severity, evidence, and a safer code example.

Threat-model this API endpoint.
List assets, trust boundaries, likely attacker goals, abuse cases,
required controls, and test cases to verify the controls.

Developer workflow reference

Testing with GitHub Copilot

Test-generation decision table

Better test prompt

Generate pytest tests for `normalize_username`.
Requirements:
- trim surrounding whitespace
- lowercase ASCII letters
- reject empty result
- reject names longer than 30 characters
- preserve digits, hyphen, and underscore
Include positive, negative, and boundary cases.
Do not change production code.

Testing traps

GitHub Copilot in the CLI

Use Copilot CLI assistance for command suggestions and explanations, especially when the task is command-line focused.

gh extension install github/gh-copilot
gh copilot suggest "find large files in this repository"
gh copilot explain "git reset --soft HEAD~1"

IDE and feature troubleshooting

Common GH-300 traps

Quick prompt recipes

Explain code

Explain the selected code for a new maintainer.
Cover purpose, inputs, outputs, side effects, dependencies,
error handling, and any risky assumptions.

Refactor safely

Refactor the selected code to reduce duplication and improve readability.
Do not change public behavior, function names, return types, or error semantics.
List assumptions and recommend tests to run.

Generate tests

Create unit tests for the selected function using the existing project test style.
Include normal cases, edge cases, invalid input, and one regression test
for the described bug. Explain why each test matters.

Debug

Given this error and selected code, identify the most likely root cause.
Provide a minimal fix, explain why it works, and list how to verify it.

Secure coding review

Review this code for security issues.
Prioritize findings by severity and exploitability.
Provide safer code only where a concrete issue exists.

Last-minute checklist

• Know when to use inline suggestions, Copilot Chat, GitHub.com features, and CLI assistance.
• Distinguish personal Copilot use from Copilot Business or Enterprise governance.
• Understand seat assignment, policy controls, public-code matching, and content exclusions.
• Remember that context quality drives answer quality.
• Use prompts with goal, context, constraints, output format, and validation.
• Treat generated code as a draft requiring review, tests, and security checks.
• Do not paste secrets, private customer data, or unnecessary sensitive logs.
• Validate commands before running them, especially destructive CLI commands.
• For business scenarios, choose centralized admin controls over individual preferences.
• For testing scenarios, ensure generated tests map to requirements and edge cases.

Practical next step

Use this Quick Reference to drill scenario questions: for each GH-300 practice item, identify the Copilot surface, the needed context, the governance control, and the validation step before choosing an answer.