Anthropic Claude Certified Architect CCAR-F

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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, and Glob--and integrates with Model Context Protocol (MCP) servers.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews--checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?

A. Build a custom MCP server wrapping Jira's API with tools designed specifically for this team's code-review workflow.
B. Use the Bash tool with curl to call Jira's REST API, including authentication headers and parsing JSON responses inline.
C. Export Jira tickets to Markdown files in the repository that the agent accesses using the Read tool.
D. Integrate an existing Jira MCP server that exposes tickets, comments, and metadata through discoverable tool interfaces.

Question #2
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.
Your invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation--for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
B. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.
C. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
D. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.

Question #3
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.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

A. Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose
B. Standardize all subagent outputs to prose summaries with inline citations
C. Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation
D. Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence

Question #4
A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?

A. Increase creativity.
B. Search social media automatically.
C. Invent a likely answer.
D. Clearly indicate insufficient information.

Question #5
Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

A. Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
B. Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
C. Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
D. Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.

Solutions: