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

1. Why is it critical to require subagents to include publication or data collection dates in their structured outputs before passing them to a downstream synthesis agent?

A) To prevent temporal differences in the underlying data from being misinterpreted by the synthesis agent as direct factual contradictions.
B) To automatically trigger the Message Batches API's 24-hour processing window logic.
C) To ensure strict ISO 8601 validation passes when using the tool_choice: " any" parameter.
D) To sort the JSON keys alphabetically and reduce context window token consumption.

2. 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) Ask the model to "find all code issues" to ensure nothing is missed.
C) Instruct the model to only report high-confidence findings.
D) Use a temperature of 0.0 to ensure deterministic outputs.

3. Under which condition is a validation-retry loop fundamentally ineffective for improving data extraction quality?

A) When the line items fail to sum to the stated total.
B) When there is a structural output error in the response.
C) When the required information is entirely absent from the provided source document.
D) When the model outputs a format mismatch.

4. When designing escalation patterns for an autonomous customer support agent, which of the following represent architectural anti- patterns that should be avoided? Choose 2 correct answers.

A) Relying on the model's self-reported confidence score to determine if human review is needed.
B) Escalating cases immediately when a customer explicitly requests to speak to a human.
C) Escalating cases when the agent detects a policy gap not covered by its instructions.
D) Escalating cases automatically based on high levels of negative customer sentiment.

5. During a long-running customer support session, the agent repeatedly summarizes the conversation history to save context space. By turn 10, the agent forgets the customer's specific account number and the exact promotional price they were quoted. How should you redesign the context management to prevent this?

A) Store the conversation in an external database and run a lexical search query on every turn.
B) Extract transactional facts into an immutable 'case facts' block positioned at the start of the context that is never summarized.
C) Restart the session from scratch after 5 turns and ask the customer to repeat their details.
D) Increase the frequency of progressive summarization to occur every 2 turns instead of 5.

Solutions: