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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, Glob) and integrates with Model Context Protocol (MCP) servers.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective?

A. Define a fixed sequence of investigation steps upfront - grep for error patterns, then read error handlers, then check database queries, then examine middleware - executing each step regardless of intermediate findings.
B. Have the agent first create a comprehensive plan mapping all code paths through the endpoint before beginning any file exploration or code reading.
C. Run parallel worker agents that simultaneously investigate all four layers, then synthesize their findings to identify where the error originates.
D. Have the agent dynamically generate investigation subtasks based on what it discovers at each step, adapting its exploration plan as new information about the error path emerges.

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, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

A. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.
B. Read the project's main entry file and follow import statements to trace where eval() might be used.
C. Use Bash to run ls -R | grep eval and search the recursively listed filenames.
D. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.

Question 3

Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API call using a Sonnet-tier Claude model. The release notes are not needed until the following morning, so results have approximately 12 hours of acceptable latency. Your team needs to reduce per-token API cost for this step while keeping the same model and prompts, with no change to the model tier or output quality. Which approach satisfies all these constraints?

A. Switch the summarization calls from the Sonnet-tier model to a Haiku-tier model to take advantage of Haiku's lower per-token rates.
B. Concatenate all 200 commit messages into a single Messages API request and have the model return all summaries in one response, because fewer requests always reduce total token cost.
C. Submit the 200 requests to the Message Batches API with unique custom_id values and retrieve the results after the batch finishes, which applies a 50% discount to all input and output tokens.
D. Issue the 200 Messages API requests in parallel using concurrent connections, because concurrency lowers the per-token price charged by the API.

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.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?

A. Parse Claude's text response with regex patterns to extract JSON objects, using retry logic for malformed responses.
B. Make two separate API calls--first extracting information as text, then asking Claude to format that text as JSON.
C. Define a tool with an input schema matching your required JSON structure and extract the data from Claude's tool_use response.
D. Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.

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.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?

A. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.
B. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
C. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
D. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.

Solutions: