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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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. 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.
B. Routing - classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
C. Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.
D. 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.

Question 2

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

Question 3

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs--for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?

A. Redesign the review as a turn-limited agentic task that can read files and search the repository, following references to verify cross-file findings.
B. Build a static dependency graph and include every file located within two dependency hops of a changed file.
C. Run separate review passes for each changed file with its direct dependants, and then aggregate and deduplicate the findings through a final consolidation pass.
D. Add instructions asking the model to list external references and reason step by step about how each change could affect unseen callers.

Question 4

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

Question 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.
Your schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?

A. Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
B. Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
C. Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
D. Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.

Solutions: