최신 Claude Certified Architect CCAR-F 무료샘플문제:

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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?

A) Pre-process source documents to standardize problematic formats before sending them for extraction.
B) Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.
C) Set temperature to 0 to eliminate output variability and ensure consistent formatting.
D) Send a follow-up request including the validation error, asking the model to correct its output.

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

3. 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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

A) Add prompt instructions to return null for any field where information is not directly stated in the source.
B) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
C) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.
D) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.

4. 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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?

A) Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
B) Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
C) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
D) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.

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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A) You check the stop_reason field in each API response-the loop continues while it equals "tool_use" and exits when it changes to "end_turn" or another terminal value.
B) You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
C) You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
D) You check whether Claude's response contains a text content block-if text is present, the agent has produced its final answer and the loop should exit.

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