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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. 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.
B. 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.
C. 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.
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

You are integrating Claude into an application written in Python. The Claude SDK provides a Python client that wraps the underlying REST API.
How would you integrate the SDK?

A. Call the REST API directly with raw HTTP requests so the application avoids the SDK's abstraction between the application code and the API.
B. Use a different LLM provider's SDK and translate the responses into Claude's API shape so the application can switch providers in the future.
C. Skip the SDK and embed Claude calls in shell commands invoked from Python, so that the application runs the calls outside the main Python process.
D. Use the Claude Python SDK and let it handle authentication, retries, and response parsing through its standard documented patterns for Python integrations.

Question 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 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 separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.
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.

Question 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. 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.
B. 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.
C. Suggest replacing the descriptions with example calls embedded in the tool definition, treating example calls as a complete substitute for the prose description.
D. Agree with the teammate because tool names are sufficient for the model to choose the right tool on every request the agent handles.

Question 5

You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

A. The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
B. Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.
C. A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.
D. A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.

Solutions: