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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 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.
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. Instruct the synthesis agent to distinguish well-established findings from contested findings explicitly, preserving each source's original uncertainty, methodology, and supporting evidence.
B. Add a verification subagent that passes claims to synthesis only when they are corroborated by at least two independent sources.
C. 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.
D. Configure subagents to report only findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.

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

Question 3

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.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

A. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
B. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
C. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
D. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.

Question 4

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.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A. Add both servers to the project-level .mcp.json file
B. Add venue server to .mcp.json and playlist server to ~/.claude.json.
C. Add venue server to ~/.claude.json and playlist server to .mcp.json
D. Add both servers to your local ~/.claude.json

Question 5

After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

A. Split the review into a finding stage whose objective is comprehensive coverage--reporting every potential issue with confidence and severity metadata--and a separate stage that verifies and thresholds those findings.
B. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.
C. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
D. Add detailed few-shot examples demonstrating bug categories Claude should flag--race conditions, null dereferences, and error-handling gaps--while retaining the high-confidence filtering instruction.

Solutions: