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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
| Useful links | Description |
|---|---|
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| Exam scoring and score reports | A score of 700 or greater is required to pass. |
| Exam sandbox | You can explore the exam environment by visiting our exam sandbox. |
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About the exam
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured as of January 2026
Audience profile
Candidates for this exam should possess expertise in using GitHub Copilot to improve software development productivity, quality, and security. This includes responsible AI use, prompt engineering, Copilot features across various plans, and privacy safeguards. Candidates should also be familiar with GitHub fundamentals and have experience with one or more programming languages.
Skills at a glance
Use GitHub Copilot responsibly (15–20%)
Use GitHub Copilot features (25–30%)
GitHub Copilot features (25–30%)
Understand GitHub Copilot data and architecture (10–15%)
Apply prompt engineering and context crafting (10–15%)
Improve developer productivity with GitHub Copilot (10–15%)
Configure privacy, content exclusions, and safeguards (10–15%)
Use GitHub Copilot responsibly (15–20%)
Understand responsible AI principles
Describe risks and limitations of Generative AI tools
Describe ethical and responsible AI usage
Identify potential harms and mitigation strategies of AI usage
Validate and operate AI tools
Explain the need to validate AI output
Identify how to operate GitHub Copilot responsibly
Use GitHub Copilot features (25–30%)
Use GitHub Copilot in the IDE
Enable Copilot in the IDE
Trigger Copilot through inline suggestions, chat, CLI, and Plan Mode
Exclude specific files or repositories (app knowledge)
Use GitHub Copilot CLI
Define GitHub Copilot CLI and how it benefits developers
Identify the steps for installing GitHub Copilot CLI
Describe key GitHub Copilot CLI features and commands
Use GitHub Copilot CLI interactively and in sessions
Generate scripts and manage files with GitHub Copilot CLI
Use GitHub Copilot features and capabilities
Use Agent Mode, Edit Mode, and MCP for enhanced development and workflows; manage Agent Sessions and delegate tasks to Sub‑Agents for optimized context usage
Use Copilot for code review and coding assistance
Utilize Spaces, Spark, Pull Request summaries, and customizable review standards via instructions files
Understand the limits, options, feedback, and commands of GitHub Copilot Chat; include prompt file reuse for consistent responses
Manage organization-wide settings and policies
Configure organization-wide policy management; enable Copilot Code Review policies and manage feature availability across IDEs and github.com
Utilize audit log events
Manage subscriptions using the REST API
Understand GitHub Copilot data and architecture (10–15%)
Describe data handling and flow
Explain data usage, flow, and sharing
Describe input processing and prompt building
Explain proxy filtering and post-processing
Understand lifecycle and limitations
Visualize code suggestion lifecycle
Describe limitations of LLMs and Copilot
Apply prompt engineering and context crafting (10–15%)
Craft effective prompts
Describe prompt structure and context
Understand how context is determined
Use zero-shot and few-shot prompting
Apply best practices for prompt crafting
Engineer prompts for performance
Explain prompt engineering principles
Describe prompt process flow and chat history usage
Improve developer productivity with GitHub Copilot (10–15%)
Enhance productivity and code quality
Use Copilot for code generation, refactoring, and documentation
Accelerate learning and reduce context switching
Generate sample data and modernize legacy code
Support testing and security
Generate unit and integration tests
Identify edge cases and write assertions
Suggest security improvements and performance optimizations
Configure privacy, content exclusions, and safeguards (10–15%)
Manage privacy settings and exclusions
Configure content exclusions and editor settings
Describe ownership and limitations of outputs
Apply safeguards and troubleshoot
Enable duplication detection and security warnings
Resolve issues with suggestions and exclusions
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course on Microsoft Learn – GitHub Copilot Fundamentals Part 1 and GitHub Copilot Fundamentals Part 2 |
| Find documentation | Responsible AI GitHub Copilot plans and features How GitHub Copilot works and handles data Prompt crafting and prompt engineering Developer use cases for AI Testing with GitHub Copilot Privacy fundamentals and content exclusions |
| Ask a question | GitHub Community Discussions |
| Get community support | GitHub Blog |
| Follow GitHub | Twitter |
| Find a video | YouTube |
Change log
This exam has changed significantly (e.g., new objectives were added, some were removed, existing objectives may have moved to different functional groups, and all were reworded) on January, 2026.