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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?

A. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.
B. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
C. Refactor the prompt-handling code in small increments as part of each new feature ticket, treating the cleanup as a side effect of feature work.
D. Move all the prompt-handling code into a single large function to reduce the number of files developers have to navigate when reading the code.

Question 3

A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

A. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.
B. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.
C. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.
D. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.

Question 4

You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?

A. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.
B. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.
C. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
D. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.

Question 5

Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?

A. Retry the same request repeatedly until valid JSON appears in the model's response, with the retry loop adding delay to the application's response time on affected requests.
B. Switch to free-form text output so the application no longer depends on JSON parsing for any of the responses it sends to downstream systems during normal operation.
C. Manually inspect every response before downstream processing so a human reviewer catches any malformed JSON before the application passes the response to downstream systems.
D. Add output validation that parses Claude's response against the expected schema and treats malformed output as a recognized error path with retry or fallback handling.

Solutions: