[100% Off] Practice Exams | Microsoft Gh-300: Github Copilot
Be prepared for the Microsoft GH-300: GitHub Copilot Exam
Description
In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.
The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.
Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.
The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item “B” last time you went through the test.
NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.
Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.
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 · Change is constant. GitHub keeps you ahead.
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
Author(s): Wade Henderson
