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

Question 2

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

Question 3

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

Question 4

You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
B. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
C. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
D. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.

Question 5

A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
B. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
C. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
D. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.

Solutions: