Microsoft Applied Skills: Accelerate AI-assisted development

Exam Overview & Audience Profile

This comprehensive exam content outline maps directly to the official learning path and interactive assessment objectives for the Microsoft Applied Skills: Accelerate AI-Assisted Development by Using GitHub Copilot credential (associated with course AZ-2007).

The credential validates a candidate's intermediate technical ability to leverage generative AI within a modern development lifecycle using GitHub Copilot and GitHub Copilot Chat inside a Visual Studio Code environment.

Target Audience: Application Developers, App Makers, and DevOps Engineers who use AI-driven code completion and chat interfaces to build, document, test, and optimize software.

Primary Environment: Visual Studio Code (VS Code) utilizing the official GitHub Copilot and GitHub Copilot Chat extensions.

Core Language Focus: General programming paradigms, with specific interactive lab scenarios tailored to C# and the C# Dev Kit.

Assessment Format: Performance-based, interactive cloud lab environment.

Duration: 2 Hours (120 minutes).

Domain 1: Get Started with GitHub Copilot & Environment Configuration

1.1 Core AI Architectures and Subscriptions

Compare and contrast the feature sets, target audiences, and enterprise governance capabilities of different subscription tiers:

GitHub Copilot Individual

GitHub Copilot Business

GitHub Copilot Enterprise

Explain the underlying technical synergy between GitHub, OpenAI models, and Microsoft Azure AI infrastructure.

Describe data privacy controls, including how telemetry, prompts, and suggestions are collected, stored, or excluded based on account configurations.

1.2 Extension Setup and IDE Customization

Install and configure the GitHub Copilot and GitHub Copilot Chat extensions within Visual Studio Code.

Authenticate the IDE environment with a valid personal, organizational, or enterprise GitHub account.

Manage extension settings in VS Code (settings.json), including enabling/disabling inline suggestions, triggering behaviors, and specifying language-specific configurations.

Troubleshoot connection, authentication, and activation issues (e.g., extensions not rendering suggestions for specific file formats or working behind enterprise proxies).

Domain 2: Explain Code by Using GitHub Copilot Chat

2.1 Code Analysis and Translation

Analyze complex, legacy, or unfamiliar blocks of code using the /explain slash command.

Leverage GitHub Copilot Chat to deconstruct intricate programming logic, data structures, and algorithmic complexities into plain language.

Execute multi-language translation tasks (e.g., explaining logic or converting logic blocks across languages like C#, JavaScript, Python, or SQL).

Identify and unpack the context of context-aware responses using variable and file references in chat windows.

2.2 Navigating Interface Elements and Scoping

Efficiently manipulate the various Copilot interfaces within VS Code:

Chat View: Side panel for high-level conversational analysis and project-wide context.

Inline Chat: Context-specific (Ctrl+I / Cmd+I) interface for localized, rapid code exploration.

Quick Chat: Floating dropdown window for immediate inquiries without shifting workspace layouts.

Use Smart Actions (e.g., right-click menu options for Copilot) to analyze highlighted snippets of text or active editor scopes.

Domain 3: Document Code by Using GitHub Copilot Tools

3.1 Inline and Method-Level Documentation

Utilize GitHub Copilot to automatically generate XML documentation comments, docstrings, and standard summary fields above methods, classes, and interfaces.

Provide natural language prompt instructions via Inline Chat to document logic workflows within complex functions.

Generate consistent, syntax-compliant parameter (<param>) and return type (<returns>) documentation based on live context.

3.2 Project-Wide Documentation and Explanatory Assets

Draft repository-level document structures, including comprehensive md markdown files, utilizing chat commands.

Instruct Copilot to generate markdown summaries of code changes, file structures, architectural data flows, and API endpoint usage instructions.

Create documentation tailored to non-technical stakeholders or team onboarding guidelines by altering the tone and constraints of the generative model prompts.

Domain 4: Develop Code Features by Using GitHub Copilot Tools

4.1 Prompt Engineering and Context Crafting

Implement prompt engineering best practices (e.g., providing clear context, setting specific constraints, assigning roles, and giving explicit output formatting rules).

Utilize the prompt process flow to guide the LLM toward generating deterministic, syntax-correct logic patterns.

Employ Context Crafting strategies to optimize Copilot suggestions:

Keep relevant files open as active tabs in VS Code to prime the context window.

Use precise file references (#file:filename) and variable references (#selection) in Copilot Chat.

Maintain clean naming conventions to maximize semantic search accuracy.

4.2 Inline Code Generation and Feature Implementation

Author structural comments (e.g., // TODO: or descriptive pseudo-code) to trigger contextually precise multi-line auto-completions.

Cycle through alternative code recommendations in the GitHub Copilot completions panel (Ctrl+Enter / Cmd+Enter) to evaluate different architectural solutions.

Develop new features, algorithms, data contracts, boilerplate code, and controller logic based on sequential, natural language inputs.

Generate high-fidelity synthetic mock data, JSON schemas, seed databases, and sample configuration files using natural language descriptions.

Domain 5: Develop Unit Tests by Using GitHub Copilot Tools

5.1 Test Framework Generation and Setup

Utilize the /tests slash command in GitHub Copilot Chat to identify targets for testing and automatically draft complete test suites.

Configure code blocks to match specific unit testing frameworks (e.g., xUnit, NUnit, or MSTest for C# environments).

Author assertions, mocking pipelines, and test setup parameters tailored to target system architectures.

5.2 Edge Case, Boundary, and Data-Driven Testing

Prompt GitHub Copilot to identify and generate tests covering edge cases, null-pointer threats, arithmetic boundaries, and invalid input exceptions.

Construct data-driven unit tests (e.g., using [Theory] and [InlineData] annotations) by instructing Copilot to generate rows of diverse, structurally sound inputs.

Identify logic gaps or blind spots in existing source code based on test coverage gaps highlighted via Copilot analysis.

Domain 6: Refactor, Debug, and Improve Code Sections

6.1 Code Refactoring and Quality Enhancements

Use the /fix command to clean syntax issues or re-engineer inefficient legacy structures.

Instruct Copilot to refactor code blocks to comply with modern design patterns, object-oriented principles (SOLID), and language-specific clean coding idioms.

Optimize runtime performance and algorithmic complexity (e.g., reducing nested loops, replacing synchronous calls with asynchronous logic) via targeted refactoring prompts.

6.2 Debugging and Defect Resolution

Leverage Copilot Chat to analyze runtime error stack traces, compiler errors, and logical bugs.

Generate iterative resolution proposals and code patches to fix identified vulnerabilities or faults.

Apply Copilot recommendations to prevent common software flaws, such as resource leaks, unhandled exceptions, and type safety issues.

6.3 Vibe Coding & Iterative App Building

Apply modern "vibe coding" practices—transitioning the developer's primary role from manual syntax compilation to systemic direction, continuous review, and high-level behavioral prompting.

Manage rapid, iterative feedback loops where Copilot continuously updates features based on the output of test cycles and active terminal feedback.

Domain 7: Privacy, Safety, and Governance Constraints

7.1 Content Exclusions and Compliance Management

Describe the mechanisms, syntax, and limitations of defining content exclusions at the repository, organization, or enterprise level.

Explain the downstream effects of content exclusions on the behavior of Copilot extensions inside code editors.

Articulate corporate ownership boundaries and compliance frameworks surrounding generated AI outputs.

7.2 Safety Safeguards and Security Filters

Explain the configuration and behavior of the Duplication Detector Filter (matching against public code repositories).

Configure  Copilot settings on  to toggle public code matching policies and collection settings for prompts and suggestions.

Explain contractual protections offered to enterprise subscribers regarding intellectual property and copyright indemnity.

Respond to security checks, warnings, and safe code alerts surfaced during code generation processes.

You may watch this complete AZ-2007 Training Prep Video which goes deeper into navigating the interactive lab environment and leveraging Copilot extensions to solve the real-world scenarios evaluated on this Microsoft Applied Skills assessment.