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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
B. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
C. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
D. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.

Question 2

A Claude application that worked well in testing is now occasionally returning outputs that mention information not present in the input. The development team initially assumed the model was hallucinating, so they asked you to troubleshoot.
What would you do first?

A. Replace the current model with a larger one to reduce the chance of hallucination, on the grounds that larger models tend to hallucinate less in typical applications.
B. Add a system prompt instruction telling the model not to invent information, on the grounds that prompt-level instructions are the fastest fix for hallucination concerns.
C. Examine production traces to identify whether the issue is hallucination by the model, context loss, prompt injection, or another failure mode before recommending a fix.
D. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.

Question 3

You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

A. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
B. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.
C. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
D. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.

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

Question 5

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. 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.
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. 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.

Solutions: