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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

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

Question 3

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

Question 4

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. 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.
C. 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.
D. 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.

Question 5

A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?

A. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.
B. Examine traces of the refused requests to identify what triggers the refusal, whether input patterns, system prompt content, or other context.
C. Test whether removing or adjusting those instructions resolves the behavior.
D. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.

Solutions: