최신Anthropic Claude Certified Architect – Foundations - CCAR-F무료샘플문제

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.

A. Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.

B. Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

C. Add prompt instructions to return null for any field where information is not directly stated in the source.

D. Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.

A. Use --continue to pick up where the most recent conversation left off

B. Use --session-id with the UUID from yesterday's session transcript file

C. Use --resume auth-deep-dive to load that specific session by name

D. Start fresh and re-read the same files

A. Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.

B. Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.

C. Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.

D. Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."

A. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.

B. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

C. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.

D. Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.

A. Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.

B. Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.

C. Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.

D. Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.

A. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.

B. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

C. Randomly sample 5% of extractions for review.

D. Route extractions for review only when downstream systems report data quality issues or processing failures.