Anthropic CCA-F 시험 개요:
| 인증 벤더: | Anthropic |
|---|---|
| 시험명: | Claude Certified Architect – 기초 과정 (CCA-F) |
| 시험 번호: | CCA-F |
| 관련 자격증: | Claude Certified Architect |
| 시험 형식: | 감독관 입회 하 시험, 시나리오 기반 문항, 객관식 문항 |
| 응시료: | USD 99 (제한된 파트너 대상 기간 동안 무료로 제공될 수 있음) |
| 자격증 유효 기간: | 공식적으로 명시되지 않음 |
| 지원 언어: | English |
| 합격 점수: | 720 / 1000 |
| 시험 시간: | 120분 |
| 실제 시험 문항 수: | 60 |
| 권장 교육: | Claude Certified Architect 학습 저장소 Claude 인증 학습 자료 |
| 시험 등록: | 공식 등록 / 접근 권한 신청 |
| 샘플 문제: | Anthropic CCA-F 샘플 문제 |
| 응시 방법: | 온라인 감독관 입회 시험 또는 파트너 기관 주관 시험 |
| 전제 조건: | Claude API, Claude Code 또는 에이전트 기반 시스템 설계에 대한 경험이 권장됨 (커뮤니티 자료 기준 약 6개월 경험 권장). |
| 공식 요강 URL: | https://anthropic.com |
Anthropic CCA-F 시험 요강 주제:
| 섹션 | 비중 | 목표 |
|---|---|---|
| 주제 1: 프롬프트 엔지니어링 및 구조화된 출력 | 20% | - 안정적인 구조화 생성
|
| 주제 2: 컨텍스트 관리 및 안정성 | 15% | - 긴 컨텍스트 최적화
|
| 주제 3: 도구 설계 및 MCP 통합 | 18% | - Model Context Protocol (MCP)
|
| 주제 4: 에이전트 아키텍처 및 오케스트레이션 | 27% | - 시스템 오케스트레이션 패턴
|
| 주제 5: Claude Code 워크플로우 및 구성 | 20% | - Claude Code 운영 패턴
|
최신 Claude Certified Architect CCA-F 무료샘플문제
문제 #1
After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?
A. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
B. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
C. Pass the full accumulated context from all prior agents.
D. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
문제 #2
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?
A. Add grounding through trusted retrieval.
B. Remove examples.
C. Increase temperature.
D. Shorten responses only.
문제 #3
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
A. The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
B. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.
C. The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
D. The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
문제 #4
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?
A. The subagents need to share a single API connection to enable automatic context sharing between invocations.
B. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
C. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.
D. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
문제 #5
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?
A. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
B. Configure the web search agent to only return results from the past 6 months.
C. Require subagents to include publication or data collection dates in their structured outputs.
D. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
질문과 대답:
| 문제 #1 정답: A | 문제 #2 정답: A | 문제 #3 정답: A | 문제 #4 정답: D | 문제 #5 정답: C |
1921 개 고객 리뷰
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