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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. 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.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?

A) Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
B) Implement a topic-classification step where the coordinator categorizes requests as "well-defined" or
"exploratory" and uses different instruction styles for each category.
C) Specify research goals and quality criteria-coverage breadth, source diversity, and recency-rather than procedural steps, allowing the subagent to determine its search strategy.
D) Remove procedural details entirely, delegating with simple goals such as "research X thoroughly" and relying on the subagent's general capabilities.

2. 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.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document-analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ±$7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35-$50 billion range.
What systematic approach best addresses this?

A) Configure subagents to report only findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
B) Add a verification subagent that passes claims to synthesis only when they are corroborated by at least two independent sources.
C) Instruct the synthesis agent to distinguish well-established findings from contested findings explicitly, preserving each source's original uncertainty, methodology, and supporting evidence.
D) Implement a confidence-calibration layer that normalizes subagent uncertainty expressions to probability scores between 0.0 and 1.0, and then calculate a confidence-weighted average.

3. Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this instruction: "Only flag critical issues that would definitely cause production failures.
Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

A) Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.
B) Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
C) Add a second review pass that rereads the diff using the same prompt and looks for anything the first pass may have missed.
D) Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.

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 pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?

A) Set tool_choice to " auto " and reorder the tool definitions so extract_metadata appears first in the tools array, since Claude prioritizes earlier-listed tools.
B) Set tool_choice to " any " so Claude must use a tool, combined with system prompt instructions prioritizing extract_metadata .
C) Set tool_choice to { " type " : " tool " , " name " : " extract_metadata " } and process the enrichment requests in subsequent turns after receiving the extracted metadata.
D) Set tool_choice to { " type " : " tool " , " name " : " extract_metadata " } for every API call in the pipeline, ensuring Claude always extracts metadata before any enrichment can occur.

5. After investigating a billing dispute for more than 25 turns, you determine that duplicate charges resulted from a payment-gateway timeout triggering retry logic. The required refund of $847 exceeds your $500 authorization limit, so you must invoke escalate_to_human. The human agent will not have access to the conversation transcript. What context should you pass to enable effective resolution?

A) The complete conversation transcript containing every message and tool result.
B) A structured summary containing the customer identifier, verified root cause, refund amount, relevant transaction identifiers, actions already attempted, and recommended next action.
C) Only the diagnosis and refund amount.
D) The customer's original complaint verbatim together with excerpts from the tool results showing the duplicate transactions.

Solutions: