Anthropic CCAR-F Exam : Claude Certified Architect - Foundations

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

During testing, when a customer says, "I need a refund for my recent purchase," the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

A. Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.
B. Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.
C. Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.
D. Change tool_choice from auto to any so Claude must call a tool on every turn.

Question 2

The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?

A. Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.
B. Have the report generator query the web search agent to re-locate sources for claims in the final report.
C. Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
D. Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).

Question 3

A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?

A. Omit examples.
B. Increase randomness.
C. Specify exact column names.
D. Remove formatting instructions.

Question 4

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
B. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
C. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
D. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

Question 5

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A. Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
B. Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis
C. Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
D. Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity

Solutions: