CCA-F認定デベロッパー、CCA-F模擬試験サンプル

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic
Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing
Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system

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CCA-F模擬試験サンプル、CCA-F受験記

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Anthropic Claude Certified Architect Foundations (CCA-F) 認定 CCA-F 試験問題 (Q30-Q35):

質問 # 30
When implementing a self-evaluation loop for structured data extraction, your agent reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this evaluation visibility gap?

A. Increasing the overall sampling size randomly to capture a broader dataset.
B. Switching entirely to the Message Batches API for asynchronous processing.
C. Executing the evaluation passes inside a PreToolUse hook.
D. Tracking accuracy by specific document type and field segment (stratified metrics).

正解：D

解説：
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures before automating high-confidence extractions.

質問 # 31
You are designing a prompt to extract structured data from unstructured documents. You decide to use tool_use with a strict JSON schem a. What specific guarantee does this approach provide?

A. It eliminates the need for validation-retry loops.
B. It guarantees the model will not hallucinate missing information.
C. It eliminates JSON syntax errors and guarantees structural compliance, but does not prevent semantic errors.
D. It guarantees both structural compliance and the semantic correctness of the extracted data.

正解：C

解説：
Using tool_use with JSON schemas guarantees structural compliance (all required fields present, valid types, syntax correct) but does NOT guarantee semantic correctness (the actual values may still be inaccurate or hallucinated). You still need semantic validation rules.

質問 # 32
When designing an automated code review system for a large repository, you notice that developers are experiencing 'alert fatigue' because the AI flags too many non-issues and stylistic preferences. What is the most effective prompt engineering technique to reduce these false positives?

A. Provide at least 15 few-shot examples covering every possible coding style in the repository.
B. Define explicit, measurable review criteria, such as flag functions exceeding 50 lines of code, instead of relying on vague instructions.
C. Instruct the model to be thorough and flag everything suspicious to ensure no bugs are missed.
D. Set the model's temperature to 0.0 to ensure deterministic output and eliminate all false positives.

正解：B

解説：
Explicit, measurable criteria produce consistent, actionable results that build developer trust and reduce false positives. Vague instructions like 'be thorough' lead to over-flagging and alert fatigue. Temperature adjustments do not fix vague criteria, and providing 15 few-shot examples bloats the prompt without proportional benefit.

質問 # 33
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A. Shorten responses only.
B. Add grounding through trusted retrieval.
C. Increase temperature.
D. Remove examples.

正解：B

解説：
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.

質問 # 34
You are tasked with using Claude Code to add comprehensive unit tests to an unfamiliar legacy codebase at quicktechie.com. Which approach represents the recommended decomposition strategy for this open-ended task?

A. Start by writing tests for the first file in the directory structure and proceed sequentially through the codebase.
B. Request Claude to automatically generate a monolithic test suite in a single prompt execution.
C. Use a fixed prompt chaining pipeline that executes npm test after generating each line of code.
D. First map the project structure, identify high-impact areas, and create a prioritized plan that adapts as dependencies are discovered.

正解：D

解説：
For open-ended and complex tasks like adding tests to a legacy codebase, the best decomposition strategy involves first mapping the structure and identifying high-impact areas, then creating a prioritized plan that can dynamically adapt as the agent discovers dependencies during implementation.

質問 # 35
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