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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?

A) Create an asynchronous orchestration layer that launches parallel threads, each running a separate coordinator-subagent pair, and then aggregates the results.
B) Add instructions explaining the performance benefits of parallel execution and request that the coordinator invoke both subagents simultaneously.
C) Structure the coordinator to emit both Agent tool calls--for web search and document analysis--in a single response message instead of separate conversation turns.
D) Switch both subagents from a Sonnet-tier model to a Haiku-tier model to reduce their individual execution times.

2. After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

A) Implement post-processing that uses keyword matching to filter out findings containing terms such as "convention," "context-dependent," or "trade-off."
B) Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.
C) Add instructions to your system prompt to "be conservative," "only flag definite issues," and
"consider that some patterns may be intentional."
D) Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.

3. You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
B) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
C) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
D) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.

4. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

A) Randomly sample 5% of extractions for review.
B) Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
C) Route extractions for review only when downstream systems report data quality issues or processing failures.
D) Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

5. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system processes two document types: standard monthly reports, which are archived after processing, and urgent exception reports, which must trigger business alerts within
30 minutes of receipt. Both use the same JSON schema. You want to minimize API costs while meeting the latency requirements.
How should you architect the processing pipeline?

A) Queue all documents and submit hourly batches, flagging urgent documents for expedited handling when batch results return.
B) Submit all documents to the real-time Messages API to ensure consistent processing latency across document types.
C) Submit all documents to the Message Batches API with custom_id values for tracking. When results arrive, immediately process urgent documents and trigger delayed alerts for exceptions.
D) Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.

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