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問題 #1

Your team uses Claude Code across multiple repositories. You want the team's rules and general coding standards to apply to all repositories, and other rules to apply only to specific repositories. The team is currently duplicating instructions across every repository's CLAUDE.md file.
How would you address this?

A. Use a CLAUDE.md hierarchy that scopes general standards broadly and project-specific context within each repository's local CLAUDE.md.
B. Move all instructions to a separate documentation site that developers consult during Claude Code sessions across all repositories.
C. Use a single repository's CLAUDE.md as the central source of truth and link to it from every other repository's CLAUDE.md file.
D. Stop using CLAUDE.md altogether and ask each developer to configure Claude Code manually for each project they work on.

問題 #2

Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?

A. Truncate the conversation so the model sees only the most recent turn during each subsequent response.
B. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
C. Reset the conversation after every turn so the model loses all prior turns when generating a response.
D. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.

問題 #3

You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?

A. Use a single Claude call for both stages, on the grounds that a single call is cheaper than multiple calls in any production Claude application setup.
B. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.
C. Use multiple Claude calls in parallel that each draft a complete ticket reply, then have a fourth Claude call select the best one to send to the customer.
D. Use a single Claude call for triage and then use a non-Claude rule-based system for response generation, on the grounds that rule-based systems are more reliable for drafting replies.

問題 #4

You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

A. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
B. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
C. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.
D. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.

問題 #5

A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
B. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
C. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
D. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.

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