다운로드 Anthropic : CCAR-F 질문 & 답 PDF & Test Software

바쁜 직장인에게 Anthropic Claude Certified Architect - Foundations 시험 준비는 결코 쉽지 않은 일입니다. PassTIP의 CCAR-F 연습문제는 출퇴근 시간에도 활용하실 수 있도록 세 가지 버전으로 제공됩니다.

Anthropic CCAR-F 시험 개요:

Anthropic CCAR-F 시험 요강 주제:

Anthropic Claude Certified Architect - Foundations 관련 궁금증을 해결해 드립니다

최신 Claude Certified Architect CCAR-F 무료샘플문제

문제 #1

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 includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?

A. Issue the 200 Messages API requests concurrently because parallel execution reduces the per- token price.
B. Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.
C. Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.
D. Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.

문제 #2

An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?

A. Retrieved documents
B. System prompt
C. User prompt only
D. API key description

문제 #3

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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

A. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
B. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.
C. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
D. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.

문제 #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 surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
B. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.
C. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
D. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.

문제 #5

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
B. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
C. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

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