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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
B. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
C. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
D. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.

Question 2

You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...

A. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.
B. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
C. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.
D. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.

Question 3

You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

A. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.
B. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.
C. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
D. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.

Question 4

Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
B. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.
C. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
D. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.

Question 5

Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?

A. Reduce the number of tool calls per task by combining several existing tools into larger, multi-purpose tools.
B. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.
C. Switch to a different agentic framework that advertises automatic context-window management as a built-in feature.
D. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.

Solutions: