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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

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 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) Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
C) Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
D) 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.

2. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?

A) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
B) Call escalate_to_human , passing only the customer's original message.
C) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
D) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .

3. 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 codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?

A) Resume the session without informing the agent about the changed files
B) Resume the session and inform the agent which specific files changed for targeted re-analysis
C) Start a fresh session to ensure the agent works with current codebase state without stale assumptions
D) Resume the session and immediately have the agent re-read all 12 previously analyzed files

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 three requirements for Claude Code's behavior in your project:
* Claude must never modify files in the db/migrations/ directory.
* Claude should prefer your custom logging module over console.log .
* All TypeScript files must be auto-formatted with Prettier after every edit.
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1.
How should you restructure these requirements across Claude Code's configuration mechanisms?

A) Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook to run Prettier after TypeScript edits.
B) Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
C) Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/ , a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
D) Rewrite all three requirements in CLAUDE.md using stronger directive language and add few-shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.

5. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process_refund immediately-but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist.
Which change directly addresses the root cause of the agent fabricating the order_id value?

A) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
B) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
C) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
D) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.

Solutions: