Anthropic CCAR-F Exam Questions - Easily Pass Your Exam

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters
Topic 2: Agentic Architecture & Orchestration	27%	- Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection
Topic 3: Context Management & Reliability	15%	- Token budget management and cost control - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Context pruning and summarization strategies
Topic 4: Prompt Engineering & Structured Output	20%	- Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies
Topic 5: Tool Design & MCP Integration	18%	- Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q49-Q54):

NEW QUESTION # 49
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system processes two document types: standard monthly reports, which are archived after processing, and urgent exception reports, which must trigger business alerts within
30 minutes of receipt. Both use the same JSON schema. You want to minimize API costs while meeting the latency requirements.
How should you architect the processing pipeline?

A. Submit all documents to the real-time Messages API to ensure consistent processing latency across document types.
B. Queue all documents and submit hourly batches, flagging urgent documents for expedited handling when batch results return.
C. Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.
D. Submit all documents to the Message Batches API with custom_id values for tracking. When results arrive, immediately process urgent documents and trigger delayed alerts for exceptions.

Answer: C

Explanation:
Option B correctly separates asynchronous bulk work from latency-sensitive operational work.
Anthropic describes the Message Batches API as a cost-effective asynchronous service for high- volume requests that do not require immediate responses. Batch requests are processed independently, normally finish within an hour, may remain pending for up to 24 hours, and are billed at 50 percent of standard API prices. Those characteristics fit standard monthly reports because they are archived after processing and have no immediate business deadline. Urgent exception reports, however, must trigger an alert within 30 minutes. The batch service cannot guarantee that deadline, even if many batches finish quickly; a workload should not use typical completion time as a service- level guarantee. Routing exceptions through the synchronous Messages API provides the real-time response path needed for alerting, while the shared JSON schema keeps downstream records consistent across both routes.

NEW QUESTION # 50
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file; unchanged files are not included. Reviews are posted asynchronously and do not block pull-request creation. Developers report that reviews consistently miss bugs involving cross-file interactions-for example, a pull request renames a function's parameters, but the review does not flag callers in other files that still use the old parameter names. Post-release analysis shows that cross-file bugs account for 35% of production incidents from reviewed pull requests. What is the most effective change to your review design?

A. Add chain-of-thought instructions asking the model to list all external references in the diff and then reason step by step about how each change might affect callers in other files.
B. Use static analysis to build a dependency graph of changed code, and then expand the prompt to include every file within two dependency hops of any changed file.
C. Redesign the review as a turn-limited agentic task in which the model can read files and search the codebase through tools, following references to verify cross-file findings.
D. Run parallel review passes for each changed file with its direct dependents included, and then aggregate and deduplicate the findings through a final summarization call.

Answer: C

Explanation:
The failure is caused by missing evidence, not insufficient reasoning over the supplied evidence. A static prompt containing only changed files cannot reliably identify callers, configuration dependencies, generated interfaces, or indirect relationships located elsewhere in the repository. Asking Claude to reason more deeply cannot recover code that it was never given.
Option A converts the review into a bounded agentic loop. Claude can search for symbol references, read relevant callers, inspect type definitions, and follow newly discovered dependencies before validating a potential defect. Anthropic describes the Claude Code agentic loop as gathering context, taking action, verifying results, and repeating based on tool feedback. A turn limit preserves predictable cost and execution time.
Option C improves coverage but imposes an arbitrary two-hop boundary and depends on the accuracy of a precomputed graph, which may omit dynamic imports, reflection, generated code, configuration references, or language-specific call relationships. Option D creates duplicated context and aggregation complexity while still limiting each reviewer to predetermined dependents. Option B changes the reasoning instructions but supplies no mechanism for checking unchanged files. Tool-enabled, just-in-time retrieval is therefore the most adaptable and reliable architecture for cross-file review.

NEW QUESTION # 51
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
B. Implement sliding window context that retains the most recent 30 turns.
C. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
D. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.

Answer: D

Explanation:
Persisting structured issue data separately allows the agent to reference critical details (like order IDs, amounts, and statuses) without relying on the full conversation history. This preserves the ability to address past issues even as token limits constrain the active conversational context.

NEW QUESTION # 52
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

A. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
B. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude' s output to schema-compliant JSON.
C. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
D. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.

Answer: B

Explanation:
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular- expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema- constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true. Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces. Option A remains vulnerable to preambles or malformed output. Option B can select a JSON-looking substring but cannot establish schema validity. Option D adds latency and cost and can still fail repeatedly. The schema-constrained tool contract is consequently the most reliable architecture available in these options.

NEW QUESTION # 53
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs--for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?

A. Add instructions asking the model to list external references and reason step by step about how each change could affect unseen callers.
B. Run separate review passes for each changed file with its direct dependants, and then aggregate and deduplicate the findings through a final consolidation pass.
C. Redesign the review as a turn-limited agentic task that can read files and search the repository, following references to verify cross-file findings.
D. Build a static dependency graph and include every file located within two dependency hops of a changed file.

Answer: C

Explanation:
Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.
Anthropic's context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context.

NEW QUESTION # 54
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