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

1. During an agentic loop execution at quicktechie.com, Claude requests to use a lookup_customer tool, but the backend database is down, resulting in an access failure. What is the architecturally correct way to return the result for the next iteration?

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

2. A pull request modifies 14 files across a application. A single-pass automated review produces superficial comments, misses obvious bugs, and provides contradictory feedback. How should the review architecture be redesigned to resolve this?

A) Require developers to manually split the PR into smaller submissions.
B) Switch to a model with a larger context window to process all files simultaneously.
C) Split the review into focused passes: analyze each file individually for local issues, followed by a separate integration pass for cross-file data flow.
D) Run three independent reviews on the full PR and only flag issues that reach consensus.

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

4. A developer grants a single Claude agent access to 18 different tools for a complex refactoring task. Production logs indicate that the agent frequently selects the wrong tool. What is the most effective architectural fix for this issue?

A) Expand the tool descriptions to include more exhaustive examples and edge cases for all 18 tools.
B) Reduce the number of tools per agent to 4-5 by distributing the rest across specialized subagents.
C) Switch to a higher-tier model with a larger context window to better process the 18 tool schemas.
D) Implement a routing layer that pre-selects tools based on keyword matching in the user's prompt.

5. How can a coordinator agent effectively execute multiple subagents in parallel to speed up a complex research task?

A) By using the fork_session command for each subagent sequentially.
B) By invoking the parallel_execute method provided natively in the Agent SDK.
C) By emitting multiple Task tool calls within a single response turn.
D) By setting the concurrency frontmatter field to a value greater than 1.

Solutions: