Developer Use Cases for AI
Free GitHub Copilot practice — 6 questions on Developer Use Cases for AI, with explanations. No sign-up. Full 12-question mixed test →
A developer joins a project and must modify a 300-line function in an unfamiliar legacy module with zero comments before making a bug fix. Which Copilot workflow BEST prepares them to safely modify the code?
/explain in Copilot Chat is purpose-built for summarizing selected code in natural language, giving the developer the context needed before making a safe edit — this is the core 'explain unfamiliar code' use case tested on the exam.
A team wants pull requests opened on GitHub.com to automatically get an AI-generated summary of the code changes to speed up review, without anyone leaving the PR page. Which Copilot capability should they use?
GitHub Copilot integrates directly into the pull request creation flow on GitHub.com, generating a summary of the diff so reviewers get context without any local tooling — matching the exact requirement of 'without leaving the PR page'.
A developer selects a function and runs the /tests command in Copilot Chat, but the generated unit tests are shallow and miss key edge cases the function actually handles. What is the MOST effective next step?
Copilot's output quality depends heavily on context in the prompt and open files; adding explicit comments about edge cases and keeping relevant test files open gives the model the signal needed to produce more relevant, thorough tests.
In Copilot Chat, which slash command is specifically designed to generate unit tests for the currently selected code?
/tests is the built-in Copilot Chat slash command that generates unit tests for the selected code block.
A developer needs to port an entire Python data-processing script to idiomatic TypeScript, preserving business logic and naming conventions of the target ecosystem. Which approach gives the MOST reliable result?
Copilot Chat with the full source file open as context and an explicit instruction on target language/conventions produces the most coherent translation because it can reason about the whole script's logic and structure at once, followed by mandatory human review and testing.
Which of the following is the LEAST appropriate use case for accepting a Copilot suggestion without rigorous independent review by a subject-matter expert?
Cryptographic implementations require formal security review and proven, audited algorithms; accepting AI-generated crypto code without expert review risks subtle vulnerabilities that are hard to detect through normal testing — this is a core responsible-AI-use boundary tested on the exam.
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