Anthropic CCDV-F : Claude Certified Developer-Foundations

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 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.
• B. 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.
• C. 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.
• D. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

• A. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
• B. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
• C. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
• D. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.

A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?

• A. Output tokens are typically billed at a lower rate than input tokens, because output tokens are cheaper to produce than input tokens are to process.
• B. Output tokens are typically billed at a higher rate than input tokens, and cost models for the application should reflect both rates separately.
• C. Output tokens are not billed at all, because cost is determined entirely by the input tokens sent to the model on each request.
• D. Output tokens are typically billed at the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

• A. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
• B. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
• C. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
• D. Increase the size of the agent's context window so it can hold the full ticket history at once.

Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

• A. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
• B. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.
• C. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.
• D. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.