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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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 extraction pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?

A. Request multiple extraction attempts per document and select the most common format.
B. Define a strict output schema and include format normalization rules in your prompt.
C. Use separate extraction calls for each field to ensure consistent handling of each type.
D. Extract data as-is and normalize formats in post-processing code after Claude returns.

Question 2

When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

A. The orchestration layer automatically routes to the next tool based on the order's status field.
B. The order details are added to the conversation and the model reasons about which action to take.
C. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
D. The agent executes the remaining steps in a tool sequence planned at the start of the request.

Question 3

An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?

A. Use Retrieval-Augmented Generation (RAG)
B. Shorten every prompt
C. Increase temperature to 1.0
D. Disable system prompts

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

Question 5

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.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
B. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
C. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
D. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Solutions: