信じられないほど的中率が高いAnthropic CCA-F試験問題集

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agentic loop design and execution lifecycle 1. Tool use decision flow (tool_use vs end_turn) 2. Multi-agent coordination and delegation patterns - System orchestration patterns 1. Task decomposition strategies 2. Hub-and-spoke agent systems
Topic 2: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tool schema design 2. MCP client/server integration patterns
Topic 3: Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management
Topic 4: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. CI/CD and workflow integration 2. Plan mode vs execution mode
Topic 5: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Schema-guided outputs 2. Prompt patterns for agents

>> CCA-F合格記 <<

CCA-F試験概要 & CCA-Fソフトウエア

当社は、CCA-Fトレーニング資料の研究と革新への資本投資を絶えず増やし、国内および国際市場でのCCA-F学習資料の影響を拡大しています。私たちのCCA-F練習の高い品質と合格率は、テストのCCA-F認定の準備をするときにクライアントが学習資料を購入することを選択する98％以上を疑問視しているためです。私たちは、業界と絶えず拡大しているクライアントベースの間で良い評判を確立しています。

Anthropic Claude Certified Architect Foundations (CCA-F) 認定 CCA-F 試験問題 (Q56-Q61):

質問 # 56
An AI extraction system processes thousands of financial documents. The dashboard displays an impressive 96% overall aggregate accuracy. However, downstream teams complain about frequent errors. What is the most likely oversight in the review and monitoring design?

A. The system is using prompt chaining instead of dynamic adaptive decomposition.
B. Aggregate accuracy metrics can mask severe per-document-type failures (e.g., invoices failing at 70% while receipts succeed at 99%).
C. The system lacks a PostToolUse hook to block large financial transactions.
D. The extraction is using JSON schema tool_use which guarantees semantic correctness but fails structural checks.

正解：B

解説：
A heavily tested exam concept is that aggregate accuracy metrics can hide critical failures in specific subsets of data (stratified metrics). Tracking accuracy per document type and field is essential before automating high-confidence extractions.

質問 # 57
Your project has React components, API handlers, and database models. Test files are spread throughout the codebase next to the code they test. How can you ensure Claude automatically applies the correct testing conventions when generating code?

A. Consolidate all conventions in the root CLAUDE .md file and rely on Claude to infer the correct section.
B. Create an MCP server that injects testing rules into the prompt manually.
C. Create rule files in . claude/rules/ with YAML frontmatter specifying glob patterns like **/*. test.tsx.
D. Place a separate CLAUDE. md file in each subdirectory containing test-specific conventions.

正解：C

解説：
Using .claude/rules/ files with glob patterns allows conventions to be automatically and conditionally applied based on file paths, regardless of directory location. This reduces token overhead while ensuring accuracy.

質問 # 58
Which of the following scenarios are best suited for prompt chaining (fixed sequential pipelines) rather than dynamic adaptive decomposition? Choose 2 correct answers.

A. An open-ended investigation into an undocumented legacy API where responses guide the next queries.
B. A monolith-to-microservices architectural migration involving dozens of interacting dependencies.
C. A data processing pipeline where raw text is summarized, then translated, and finally formatted into JSON.
D. A predictable, multi-aspect security review where the sequence of code analysis steps is static and predefined.

正解：C、D

解説：
Prompt chaining is ideal for predictable, linear workflows with static sequences of steps, such as multi-aspect code reviews following a checklist, or deterministic data processing pipelines (summarize -> translate -> format). Open-ended investigations and complex migrations require dynamic adaptive decomposition.

質問 # 59
Your agent achieves a low first-contact resolution rate because it attempts to autonomously handle complex policy exceptions, while simultaneously escalating straightforward tasks. What is the most effective approach to improve this escalation calibration?

A. Implement sentiment analysis to detect frustration levels and automatically escalate when they are high.
B. Require the agent to self-report a confidence score (1-10) and route to humans when confidence falls below 7.
C. Deploy a separate classifier model to predict which requests need escalation before the main agent begins.
D. Add explicit escalation criteria to the system prompt with few-shot examples demonstrating when to escalate versus resolve autonomously.

正解：D

解説：
Adding explicit escalation criteria with few-shot examples directly addresses unclear decision boundaries. Self-reported confidence scores are poorly calibrated, sentiment analysis does not measure complexity, and a separate classifier is over-engineered before trying prompt optimization.

質問 # 60
Which statement BEST describes Retrieval-Augmented Generation?

A. It permanently retrains Claude.
B. It supplies relevant external knowledge during inference.
C. It changes token pricing.
D. It compresses model weights.

正解：B

解説：
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.

質問 # 61
......

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