Practice Exams | Microsoft GH-300: GitHub Copilot

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 glanceUse 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 principlesDescribe risks and limitations of Generative AI toolsDescribe ethical and responsible AI usageIdentify potential harms and mitigation strategies of AI usageValidate and operate AI toolsExplain the need to validate AI outputIdentify how to operate GitHub Copilot responsiblyUse GitHub Copilot features (25–30%)Use GitHub Copilot in the IDEEnable Copilot in the IDETrigger Copilot through inline suggestions, chat, CLI, and Plan ModeExclude specific files or repositories (app knowledge)Use GitHub Copilot CLIDefine GitHub Copilot CLI and how it benefits developersIdentify the steps for installing GitHub Copilot CLIDescribe key GitHub Copilot CLI features and commandsUse GitHub Copilot CLI interactively and in sessionsGenerate scripts and manage files with GitHub Copilot CLIUse GitHub Copilot features and capabilitiesUse Agent Mode, Edit Mode, and MCP for enhanced development and workflows; manage Agent Sessions and delegate tasks to Sub‑Agents for optimized context usageUse Copilot for code review and coding assistanceUtilize Spaces, Spark, Pull Request summaries, and customizable review standards via instructions filesUnderstand the limits, options, feedback, and commands of GitHub Copilot Chat; include prompt file reuse for consistent responsesManage organization-wide settings and policiesConfigure 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 eventsManage subscriptions using the REST APIUnderstand GitHub Copilot data and architecture (10–15%)Describe data handling and flowExplain data usage, flow, and sharingDescribe input processing and prompt buildingExplain proxy filtering and post-processingUnderstand lifecycle and limitationsVisualize code suggestion lifecycleDescribe limitations of LLMs and CopilotApply prompt engineering and context crafting (10–15%)Craft effective promptsDescribe prompt structure and contextUnderstand how context is determinedUse zero-shot and few-shot promptingApply best practices for prompt craftingEngineer prompts for performanceExplain prompt engineering principlesDescribe prompt process flow and chat history usageImprove developer productivity with GitHub Copilot (10–15%)Enhance productivity and code qualityUse Copilot for code generation, refactoring, and documentationAccelerate learning and reduce context switchingGenerate sample data and modernize legacy codeSupport testing and securityGenerate unit and integration testsIdentify edge cases and write assertionsSuggest security improvements and performance optimizationsConfigure privacy, content exclusions, and safeguards (10–15%)Manage privacy settings and exclusionsConfigure content exclusions and editor settingsDescribe ownership and limitations of outputsApply safeguards and troubleshootEnable duplication detection and security warningsResolve issues with suggestions and exclusions