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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
B. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
C. The subagents need to share a single API connection to enable automatic context sharing between invocations.
D. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.

Question 2

Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

A. Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
B. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.
C. Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
D. Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.

Question 3

Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?

A. Add retry logic that detects truncated JSON and resends the request with instructions to report only critical-and high-severity findings.
B. Switch from tool_use to prompting Claude to return findings as a Markdown list.
C. Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
D. Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.

Question 4

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?

A. Orchestrator-workers - have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.
B. Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.
C. Prompt chaining - break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
D. Routing - classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.

Question 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.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but
5% of extractions still contain empty arrays or null values for required fields such as citations and methodology. Spot-checking reveals that the source documents contain this information, but in varied formats--inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?

A. Build a regex-based post-processing layer that scans source documents for citation patterns and methodology keywords, populating empty fields when the model fails to extract them.
B. Add few-shot examples demonstrating extractions from documents with varied structures, showing how to identify citations in different formats and locate methodology details across section types.
C. Modify the schema to make citations and methodology optional, and flag incomplete records for manual review instead of failing validation.
D. Implement retry logic that resends requests when validation detects empty required fields.

Solutions: