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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?

A. Process tickets individually and combine summaries.
B. Remove system prompts.
C. Increase temperature.
D. Send all tickets in one request.

Question 2

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 asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?

A. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
B. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top-level directory regardless of code complexity or business importance.
C. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning.
D. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically.

Question 3

The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The verification tool is clearing the agent's internal state after each successful validation step.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D. The conversation history isn't being passed in subsequent API requests.

Question 4

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 system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence 90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high-confidence extractions.
Before deploying, what validation step is most critical?

A. Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.
B. Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.
C. Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.
D. Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.

Question 5

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. 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.
C. 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.
D. Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.

Solutions: