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Anthropic Claude Certified Architect – Foundations 認定 CCAR-F 試験問題:

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.
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) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
C) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
D) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

2. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires.
What's the most effective way to start your iterative workflow?

A) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
B) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
C) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
D) Use plan mode to analyze the current /products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

3. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields.
What's the most effective way to iterate toward a working solution?

A) Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.
B) Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
C) Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
D) Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.

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.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness. Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR.
Which task decomposition pattern is most appropriate for this workflow?

A) Routing-classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
B) Single comprehensive prompt-include all three instructions in one prompt and let the model handle all three aspects simultaneously.
C) Prompt chaining-break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
D) Orchestrator-workers-have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.

5. 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.
You've configured your Claude agent with three MCP servers: one for git operations, one for Jira ticket management, and one for documentation search.
When a user asks the agent to "create a branch for JIRA-123 and add documentation links to the ticket," how does the agent access tools across these servers?

A) Tools from all configured MCP servers are discovered at connection time and available simultaneously to the agent.
B) The agent queries each server sequentially to determine which handles each tool, routing calls based on tool name prefixes.
C) You must specify which MCP server to use for each turn, and the agent can only access one server's tools at a time.
D) The agent automatically selects the most relevant server based on the request and loads only that server' s tools.

質問と回答：