CCDV-F : Claude Certified Developer-Foundations

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Anthropic CCDV-F 試験シラバストピック:

Anthropic Claude Certified Developer-Foundations 認定 CCDV-F 試験問題：

問題 #1
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 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.
B. 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.
C. 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.
D. 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.

問題 #2
You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...

A. Operating and maintaining the system, including monitoring, responding to issues, and planning evolution.
B. Running a formal post-deployment review that assesses the development phase before the team does any further work on the system.
C. Handing the system over to a separate operations team that will manage it independently of the development team going forward.
D. Retiring the system, since shipping is the final lifecycle stage for any application that reaches production.

問題 #3
Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?

A. Apply a checkpointing pattern that persists the agent's intermediate state and reloads that state when resuming an interrupted task.
B. Run two copies of the agent in parallel for every task and use whichever one finishes first as the source of truth.
C. Restart the task from the beginning whenever a process is interrupted.
D. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.

問題 #4
A teammate has asked you to explain why your Claude agent's tools include detailed descriptions in the tool definition, even when the tool name is already descriptive. The teammate suggests removing the descriptions to simplify the tool definitions.
How would you respond?

A. Suggest moving the descriptions out of the tool definition and into a separate documentation file the team maintains so the tool definitions stay short and the descriptions remain available.
B. Explain that the model uses the tool description to decide when to call the tool, and descriptions disambiguate cases where the tool name is not enough.
C. Agree with the teammate because tool names are sufficient for the model to choose the right tool on every request the agent handles.
D. Suggest replacing the descriptions with example calls embedded in the tool definition, treating example calls as a complete substitute for the prose description.

問題 #5
You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

A. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
B. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
C. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
D. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.

解説：