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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?

A. Add more tools to cover every variation of the requests the agent handles, on the grounds that more tools give the agent more accurate options to choose from.
B. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.
C. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
D. Add detailed examples to each tool's description so the agent can match incoming requests to the right tool by example, treating the examples as the team's selection mechanism.

Question 2

You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?

A. Define state reset triggers tied to specific application events but apply them across all sessions globally, with no per-session expiration rules.
B. Define explicit session expiration rules, state reset triggers, and rules for starting fresh sessions so stale context does not leak into new conversations.
C. Define a single short session timeout that applies across all conversations and treat the timeout as the application's complete session lifecycle mechanism.
D. Define explicit session expiration rules but rely on users to start new conversations when they want fresh context, with no automatic reset triggers in the application.

Question 3

The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

A. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.
B. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
C. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
D. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.

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. 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.
B. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
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

You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?

A. A streaming pattern that emits partial decisions as the agent processes each claim, refining the output until a final decision emerges from the stream.
B. A single tool-use loop pattern that gives one agent access to all the tools needed for fact extraction, eligibility evaluation, and decision-making.
C. A linear chain pattern that processes every claim through fact extraction, then eligibility evaluation, then decision, with no return paths between subtasks.
D. A graph-based pattern that lets the agent move between subtasks based on the state of each claim, with each subtask evaluated against its own criteria.

Solutions: