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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are choosing between using STDIO-based communication and HTTP-based communication for an MCP server. The server will be invoked by a Claude Code session running locally.
Which communication pattern would you use?

A. HTTP polling against the MCP server, treating HTTP as a substitute for the standard MCP communication patterns.
B. HTTP on the grounds that HTTP is preferable to STDIO in any context the application might encounter during normal operation.
C. STDIO because STDIO is well-suited to local process communication and avoids unnecessary network setup.
D. STDIO and HTTP simultaneously for redundancy, with the team running two communication patterns at the same time across Claude Code sessions.

Question 2

You are integrating Claude into an application written in Python. The Claude SDK provides a Python client that wraps the underlying REST API.
How would you integrate the SDK?

A. Use the Claude Python SDK and let it handle authentication, retries, and response parsing through its standard documented patterns for Python integrations.
B. Skip the SDK and embed Claude calls in shell commands invoked from Python, so that the application runs the calls outside the main Python process.
C. Call the REST API directly with raw HTTP requests so the application avoids the SDK's abstraction between the application code and the API.
D. Use a different LLM provider's SDK and translate the responses into Claude's API shape so the application can switch providers in the future.

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 prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
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. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
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 4

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

A. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
B. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.
C. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
D. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.

Question 5

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

Solutions: