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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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Create summaries of all source files before exploration begins, loading only these compressed representations into context.
B. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
C. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
D. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.

Question 2

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 invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A. Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.
B. Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
C. Add a "calculated_total" field where the model sums extracted line items alongside a
"stated_total" field. Flag records for human review when values differ.
D. Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.

Question 3

During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?

A. Use the --model flag to select a smaller, less expensive model so that every iteration uses fewer tokens and costs less.
B. Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap agentic turns and expenditure.
C. Set --permission-mode dontAsk to automatically deny tool-permission requests that are not in the explicitly allowed set.
D. Set timeout-minutes: 5 on the GitHub Actions step and monitor per-run costs through the Anthropic Console usage dashboard.

Question 4

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.

Question 5

Your CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day through the synchronous API. Reviews are non-blocking-- developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API because it offers a 50% cost reduction. What factor most determines whether batch processing is appropriate for this use case?

A. Whether reducing per-review latency from 30?0 seconds to near-instantaneous delivery matters to your workflow.
B. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.
C. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.
D. Whether each review can be structured as a single request without multi-turn refinement.

Solutions: