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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

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

2. 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 system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2-3 attempts.
For which failure pattern would additional retries be LEAST effective?

A) The model extracts dates as ISO 8601 datetime strings ("2023-03-15T00:00:00Z") when the schema requires only the date portion (YYYY-MM-DD).
B) The model extracts "et al." for co-authors when the full list exists only in an external document not in the input.
C) The model extracts keywords as a nested object organized by category when the schema requires a flat array of strings.
D) The model extracts citation counts as locale-formatted strings ("1,234") when the schema requires integers.

3. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?

A) Start direct execution with WebSockets, then refactor if infrastructure issues arise.
B) Enter plan mode to explore the architecture, evaluate trade-offs, and present options for team approval before implementing.
C) Use direct execution to implement polling first, then evaluate whether to upgrade to WebSockets later.
D) Use direct execution with a prompt asking Claude to analyze all approaches and implement the one it determines is best.

4. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?

A) The verification tool is clearing the agent's internal state after each successful validation step.
B) The conversation history isn't being passed in subsequent API requests.
C) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D) The prompt lacks instructions telling Claude to remember information across multiple exchanges.

5. 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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?

A) Set temperature to 0 to eliminate output variability and ensure consistent formatting.
B) Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.
C) Pre-process source documents to standardize problematic formats before sending them for extraction.
D) Send a follow-up request including the validation error, asking the model to correct its output.

Solutions: