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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation
Topic 2: Tool Design & MCP Integration	18%	- Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability
Topic 3: Claude Code Configuration & Workflows	20%	- Integrating Claude Code into development processes - Developer productivity workflows - Claude Code usage and configuration
Topic 4: Agentic Architecture & Orchestration	27%	- Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows
Topic 5: Context Management & Reliability	15%	- Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations

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CCAR-F Deutsch Prüfung - CCAR-F Prüfungsfragen

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Anthropic Claude Certified Architect - Foundations CCAR-F Prüfungsfragen mit Lösungen (Q72-Q77):

72. Frage
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.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?

A. Prioritize the review of all extractions where required fields are empty or explicitly marked as not found.
B. Review all extractions from documents with formatting anomalies, such as unusual layouts or mixed content types.
C. Randomly sample 20% of extractions for review, using corrections to track accuracy and identify error patterns.
D. Have the model output field-level confidence scores, and then calibrate review thresholds using a labeled validation set.

Antwort: D

Begründung:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly. JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible.
Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest- confidence records or fields first.

73. Frage
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. 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.
B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
C. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
D. 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.

Antwort: D

Begründung:
The failure originates from an underspecified tool contract. A precise description tells Claude where the required parameter must come from and prevents it from fabricating an identifier.
Server-side validation in D is still valuable as a safeguard, but it detects the error rather than addressing the agent's decision-making. Anthropic identifies detailed tool and parameter descriptions as the most important factor in reliable tool use.

74. Frage
Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A. Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
B. Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
C. Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
D. Return fields organized into verified and needs_verification objects based on confidence thresholds.

Antwort: A

Begründung:
Including a request_review flag based on confidence thresholds, along with reasons for review, gives the agent clear guidance on when human intervention is needed. This reduces both unnecessary reviews and errors caused by low-confidence extractions, improving overall reliability and efficiency.

75. Frage
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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing.
Which configuration approach correctly applies MCP server scopes?

A. Add both servers to your local ~/.claude.json .
B. Add the venue server to .mcp.json and the playlist server to ~/.claude.json .
C. Add the venue server to ~/.claude.json and the playlist server to .mcp.json .
D. Add both servers to the project-level .mcp.json file.

Antwort: B

Begründung:
The venue server must be available to every project contributor, so it belongs at project scope. Claude Code stores project-scoped MCP configurations in the repository-root .mcp.json file, which can be committed to version control and shared with the team.
The experimental playlist server should remain private. Local-scoped MCP servers are stored under the current project's entry in ~/.claude.json , while user-scoped servers are also stored in ~/.claude.json but load across all projects. Because this server is described as a personal experiment, either private scope is conceptually appropriate; among the listed choices, option B correctly separates shared project configuration from private personal configuration. ( https://code.claude.com/docs/en/mcp ) Option A prevents the team from receiving the venue configuration. Option C reverses the intended visibility.
Option D exposes the experimental server configuration to other contributors and may commit machine- specific commands or credentials.
The preferred commands would specify --scope project for the shared venue server and --scope local for an experiment limited to the current repository. Project-scoped servers also require user approval before Claude Code activates configurations received through .mcp.json .
Official references/topics: MCP Installation Scopes; Project Scope; Local Scope; .mcp.json ; ~/.claude.json .

76. Frage
During testing, when a customer says, "I need a refund for my recent purchase," the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

A. Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.
B. Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.
C. Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.
D. Change tool_choice from auto to any so Claude must call a tool on every turn.

Antwort: B

Begründung:
Option A corrects the missing tool contract that permits Claude to guess a required parameter.
The process_refund description should state its prerequisite explicitly: invoke lookup_order, use only the exact order_id returned by that successful call, and ask for additional identifying information when lookup cannot resolve an order. An input example can demonstrate the correct two-tool sequence.
Anthropic's tool-definition guidance identifies detailed descriptions as the most important factor in tool performance. Descriptions should explain when a tool should and should not be used, what every parameter means, and all important limitations. The tool-use overview confirms that Claude selects and populates tools based on the request and their descriptions.

77. Frage
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