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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
B. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
C. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.
D. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.

Question 2

Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
B. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
C. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
D. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.

Question 3

Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...

A. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.
B. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
C. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.
D. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.

Question 4

You are setting up Claude Code for a new project repository. Your team has shared coding standards, preferred libraries, and project-specific context that every developer working on the repository should have available when they use Claude Code.
How would you set this up?

A. Configure Claude Code through environment variables that each developer sets on their own machine when they begin working in the repository.
B. Initialize Claude Code in the repository and document the standards and project-specific context in a CLAUDE.md file at the repository root.
C. Document the standards in a separate wiki page maintained outside the repository so the documentation stays decoupled from the source code.
D. Add the coding standards and project context to the repository's existing README file and direct developers to reference it when starting Claude Code sessions.

Question 5

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

Solutions: