GitHub Copilot certification
A shorter track for teams that already have GitHub Copilot switched on and want everyone using it the same, deliberate way.
- Levels
- Foundations
- Format
- Self-paced + live clinic
- Assessment
- Scenario review
Who this is for
- Engineering teams standardising on GitHub Copilot as the default assistant
- New hires being onboarded onto a Copilot-enabled codebase
Syllabus
Context quality
What Copilot needs open, named, and commented to suggest something usable.
Accepting vs. editing
When to take a suggestion as-is, edit it, or reject it outright — with real examples of each.
Assessment
A scenario review: walk an assessor through five real suggestions and justify each call.
Certification landscape
Where this sits next to GitHub's own exam
GitHub's own certification is real and open to the public: the GitHub Copilot exam (GH-300), an intermediate-level, proctored exam scheduled through Pearson VUE on Microsoft Learn. It checks knowledge of Copilot's features, responsible use, prompt engineering, data handling and privacy settings. What it doesn't do is watch an engineer decide whether to accept, edit or reject a suggestion in your codebase. This track tests that judgment directly, on your own code.
Frequently asked questions
Is this the same as GitHub's GH-300 exam?
No. GH-300 is a real, proctored, publicly available exam that checks what someone knows about Copilot's features, responsible use and privacy settings. This track assesses how they actually use Copilot on your code: an assessor reviews the calls they make on real suggestions.
Is this about Microsoft 365 Copilot?
No. Microsoft 365 Copilot (in Word, Outlook, Excel and Teams) is a different product with its own credentials, such as the AB-900 administration exam. This track is for GitHub Copilot in the editor. If you need Microsoft 365 Copilot usage assessed against your own workflows, we can scope a custom track.
What does the assessment actually test?
A scenario review: the candidate walks an assessor through five real Copilot suggestions from their own work and justifies each call — accept as-is, edit, or reject — against a rubric built around your team's codebase.