Claude Certified Architect Practice Exam and Questions (CCAR-F, CCA-F)

Question 1: Agentic Architecture

A travel booking agent has tools to search flights, check hotel availability, and execute bookings. It is configured to automatically book the lowest-cost itinerary within a user's $2,000 budget as soon as one is found. A user reports the agent booked the wrong dates. What architectural change most directly prevents this class of failure?

• A. Replace the auto-execute booking tool with a propose_itinerary tool that returns a confirmation request to the user before any booking is made.
• B. Insert a second-model validation step: a reviewing subagent must approve the proposed itinerary before the orchestrator invokes the booking tool.
• C. Narrow the agent's tool set to search and booking only, so a smaller action space leaves fewer ways for a date to go wrong.
• D. Strengthen the system prompt with a pre-booking checklist that makes the agent restate the travel dates and confirm they match the request before calling the booking tool.

Correct answer: A

A flight booking is a consequential, hard-to-reverse action. Task Statement 1.5 of the exam guide covers tool call interception hooks that block policy-violating actions and redirect them to a human escalation workflow, and Task Statement 1.4 notes that prompt instructions alone carry a non-zero failure rate where deterministic compliance is required. Replacing auto-execute with a propose-and-confirm pattern inserts a human checkpoint at exactly the right moment: before commitment, not after.

Why B is wrong: A validation subagent is still an autonomous model reviewing another model's decision. It adds a layer but doesn't resolve the core problem: the booking still executes without human approval. Model-on-model review does not substitute for human-in-the-loop oversight on consequential actions.

Why C is wrong: Reducing the tool count doesn't affect execution behavior. The agent still auto-executes bookings. It just has fewer other tools while doing so. This doesn't address the reversibility or confirmation gap.

Why D is wrong: Prompts are probabilistic. Adding caution instructions influences Claude's behavior but cannot guarantee it. A prompt-based fix for a safety-critical flow is architecturally incorrect. The guarantee must come from the infrastructure layer, not the instruction layer.

Question 4: Claude Code Configuration

A development team wants Claude Code to always run the test suite before committing, use the company linting config at .eslintrc.company.json, and never modify any files in the /contracts/ directory. Where should these instructions be placed so all developers on the team get them automatically?

• A. In the project's .claude/settings.json, committed for team-wide configuration.
• B. In a CLAUDE.md file at the project root, committed to the repository.
• C. In a .claude/settings.local.json at the project root, kept out of git.
• D. In each developer's global ~/.claude/CLAUDE.md file.

Correct answer: B

Project-level behavioral instructions (coding standards, workflow rules, file restrictions) belong in CLAUDE.md at the project root. Committed to the repository, this file is automatically picked up by Claude Code for every developer who clones the project. This is the designed mechanism for sharing persistent natural-language instructions across a team.

Why A is wrong: settings.json controls tool permissions, hooks, and which .mcp.json servers are approved, not natural-language behavioral instructions. It is not the correct place for "run tests before committing" or "avoid /contracts/" style guidance.

Why C is wrong: settings.local.json is for local overrides that are intentionally not committed. Instructions that need to apply to the whole team must be in a committed file.

Why D is wrong: The global ~/.claude/CLAUDE.md applies to every project on a developer's machine. Putting project-specific rules there would apply them to unrelated projects and doesn't help other developers. Each person's global file is local to their machine.

Question 8: Tool Design & MCP

A tool called get_customer_records accepts a customer ID and a date range. Which JSON Schema definition will enable Claude to call this tool most reliably?

• A. {"$schema": "https://json-schema.org/draft/2020-12/schema", "title": "get_customer_records", "type": "object", "properties": {"customer_id": {"type": "string", "minLength": 1, "maxLength": 64}, "start_date": {"type": ["string", "null"], "minLength": 1, "maxLength": 32}, "end_date": {"type": ["string", "null"], "minLength": 1, "maxLength": 32}}, "required": ["customer_id"], "additionalProperties": false}
• B. {"type": "object", "description": "Arguments for get_customer_records. The tool accepts any JSON object: pass the customer and the period in whichever fields seem most natural, using any property names, and the server maps them onto its own parameters. Unrecognised fields are ignored rather than rejected, so an over-specified call is always safe, and no properties are declared so the schema never needs updating when the record store changes.", "additionalProperties": true}
• C. {"type": "object", "properties": {"customer_id": {"type": "string", "description": "The unique customer identifier (e.g. CUST-12345)"}, "start_date": {"type": "string", "format": "date", "description": "Start of the date range in YYYY-MM-DD format"}, "end_date": {"type": "string", "format": "date", "description": "End of the date range in YYYY-MM-DD format"}}, "required": ["customer_id", "start_date", "end_date"]}
• D. {"type": "object", "properties": {"query": {"type": "string", "description": "A free-text request naming the customer and the period, for example 'records for CUST-12345 between 1 March and 31 March', which the tool parses on receipt"}}, "required": ["query"]}

Correct answer: C

An effective tool schema names each parameter explicitly, includes descriptions that tell the model what each parameter means and what format is expected, specifies format hints where relevant (e.g., "format": "date" for ISO 8601 dates), and declares all required fields. Option C does all of this. The descriptions and format hints are what allow Claude to populate parameters correctly from natural-language user requests. Without them, Claude must infer format and meaning from the parameter name alone.

Why A is wrong: This schema has the correct parameters but no descriptions and marks the date fields as optional. A date range tool without required start and end dates is inconsistently defined. The fields are conceptually mandatory for the query to make sense. Absent descriptions, Claude must guess the expected format for each field.

Why B is wrong: A schema that declares no properties provides no usable guidance: the description invites Claude to invent field names, and the tool cannot reliably parse whatever it invents.

Why D is wrong: Collapsing all input into a single query string removes the structure the tool needs. Claude cannot reliably produce a correctly formatted customer ID and date range from a single untyped string parameter, and the downstream system cannot parse structured data from freeform text.

Scenario-Based Design

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Multiple-Choice and Multiple-Response

Multiple-choice and multiple-response questions, matching the mixed format described in the official exam guide. Multiple-response items tell you upfront how many responses to select.

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400 questions across the five CCAR-F exam domains, weighted the way Section 4 of the official exam guide weights them. Every question has a written explanation of why the right answer is right.

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