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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Which prompt strategy is most effective at reducing false positives in an automated code review system?

A) Provide explicit, measurable criteria like "flag functions exceeding 50 lines".
B) Instruct the model to only report high-confidence findings.
C) Ask the model to "find all code issues" to ensure nothing is missed.
D) Use a temperature of 0.0 to ensure deterministic outputs.

2. An agent is designed to self-evaluate its confidence before deciding whether to route an ambiguous task to a human reviewer. Why is relying on the model's self-reported conversational confidence score for escalation routing considered a flawed approach?

A) It requires enabling the extended_thinking parameter, which introduces unacceptable latency.
B) It forces the agent to use the tool_choice: "auto" setting, bypassing deterministic programmatic hooks.
C) The model's self-reported conversational confidence scores are poorly calibrated and cannot be relied upon for production routing decisions.
D) Confidence scoring requires fine-tuning a custom model to accurately match the enterprise's risk tolerance.

3. An MCP server offers a templated resource to fetch specific documentation pages by ID. What is the correct URI syntax pattern utilized by the MCP Python SDK to represent this parameterized resource?

A) Option A
B) Option D
C) Option C
D) Option B

4. Which of the following statements accurately describe the characteristics and constraints of the Message Batches API? Choose 2 correct answers.

A) It provides a guaranteed I-hour latency SLA for enterprise tier accounts.
B) It offers a 50% cost savings compared to standard synchronous API calls.
C) It is optimized to support interactive, real-time chatbot workflows at scale.
D) It uses custom_id fields to correlate asynchronous requests with their respective responses.

5. To keep the main agent's context clean while investigating a complex authentication flow, which architectural pattern is highly recommended?

A) Use the Message Batches API to process the exploration synchronously.
B) Apply progressive summarization to the main conversation history every 2 turns.
C) Use the fork_session command to duplicate the main agent permanently.
D) Spawn a subagent to isolate the verbose exploration output, returning only a focused summary to the main agent.

Solutions: