Anthropic CCAR-F

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question #1
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 monorepo contains shared coding standards in /docs/standards/ : security-rules.md (for services handling user data), testing-petterns.md (for all packages), and api-conventions.md (for API-facing services). Your 15 packages are organized by feature domain ( /packages/auth/,
/packages/billing/, /packages/notifications/ , etc.) without naming conventions indicating which handle user data or expose APIs. Package maintainers are expected to configure their own local development settings, as they understand their package's domain requirements. Currently, all package CLAUDE.md files duplicate all three standards, applying irrelevant guidance. What's the most effective approach?

A. Use @imports in each package's CLAUDE.md to reference only the specific standard files relevant to that package, based on the maintainer's domain knowledge.
B. Put all standards in the root CLAUDE.md with override instructions like "ignore security-rules.md when working in packages that don't handle user data."
C. Create a shared-stendards.md that uses @imports to combine all three standards, then have each package's CLAUDE.md import that combined file.
D. Create .claude/rules/ files for each standard with YAML frontmatter paths listing every package directory where that standard should apply.

Question #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 monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.
B. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.
C. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.
D. Structure the coordinator to emit both Agent tool calls--formerly Task tool calls--in a single response rather than across separate conversation turns.

Question #3
Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A. Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.
B. Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.
C. Return a result object with isError: true and a message explaining no products matched.
D. Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.

Question #4
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?

A. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
B. Include the complete findings from both subagents directly in the synthesis subagent's prompt
C. Provide the subagent with tool definitions that allow it to request outputs from other subagents via callbacks
D. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator

Question #5
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

A. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
B. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
C. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
D. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes

Solutions: