CCAR-F試験の準備方法 |検証するCCAR-F模擬モード試験 |認定するClaude Certified Architect - Foundationsファンデーション

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters
Topic 2: Context Management & Reliability	15%	- Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience
Topic 3: Tool Design & MCP Integration	18%	- Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0
Topic 4: Agentic Architecture & Orchestration	27%	- Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection
Topic 5: Prompt Engineering & Structured Output	20%	- Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q80-Q85):

質問 # 80
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to understand how the caching layer works before adding a new cache-invalidation trigger. Initial Grep searches show that caching logic spans 15 files containing decorators, middleware, and service classes-approximately 8,000 lines in total.
What is the most effective next step for building understanding while managing context constraints?

A. Use Read to load all 15 files sequentially and build a complete understanding of the caching implementation.
B. Analyze imports and class hierarchies to identify the base cache class, read that file to understand its interface, and then trace the specific invalidation implementations.
C. Use Grep to search for invalidate and expire , and then read only the matching line ranges with minimal surrounding context.
D. Use Glob to find files matching common caching patterns such as *cache*.py or caching/ , read the largest files first, and inspect smaller files afterward.

正解：B

解説：
Option A establishes the caching architecture before consuming thousands of implementation lines. The base class or protocol reveals the supported operations, lifecycle, cache-key rules, and invalidation contract.
Imports, subclasses, registrations, and call sites can then direct the agent toward only those concrete implementations relevant to the proposed trigger. Anthropic's effective context-engineering guidance explains that context is finite and that recall degrades as irrelevant tokens accumulate. It identifies Glob and Grep as mechanisms for retrieving code just in time rather than loading an entire repository upfront. Option B assumes file size or naming determines architectural importance; a small interface may define the system while a large service file contains incidental caching calls. Option C loads approximately 8,000 lines before establishing which portions matter. Option D is overly lexical and can miss invalidation implemented through event publication, key-version changes, inherited methods, or generically named hooks. Dependency-guided exploration provides a compact conceptual model first and expands the investigation only when concrete relationships show that additional files are relevant.

質問 # 81
Production monitoring shows that follow-up queries like "summarize what we learned about market trends" consistently take 40+ seconds. Investigation reveals the coordinator spawns the synthesis subagent for each summarization request, passing 80K+ tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research.
What's the most effective way to improve response time for these follow-up summaries?

A. Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
B. Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
C. Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.
D. Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.

正解：B

解説：
The coordinator already holds the accumulated findings and can perform simple summarizations directly. Avoiding repeated spawning of the synthesis subagent for straightforward queries reduces token processing and latency, improving response speed while maintaining accuracy.

質問 # 82
Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A. Return a result object with isError: true and a message explaining no products matched.
B. Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.
C. Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.
D. Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.

正解：C

解説：
A zero-result search is a successful tool execution with an empty result set, not an error.
Returning a structured success indicator with the results array helps the agent distinguish valid
"no matches found" outcomes from actual tool or API failures, preventing unnecessary retries.

質問 # 83
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%. How should you address this trade- off to improve detection across both categories?

A. Split the review into separate focused prompts-one for security and API design and another for business logic-each with dedicated examples, and then consolidate the findings before posting.
B. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
C. Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
D. Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in a single prompt and eliminate the recall trade-off.

正解：A

解説：
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
Option A increases context volume without resolving competition between the review objectives; irrelevant repository content may further dilute attention. Option B may improve known quiz-scoring cases but can overfit the reviewer to an incomplete checklist and does not restore API-design recall. Option D assumes that additional model capability will eliminate prompt interference without supporting evaluation evidence.
Separate specialized review passes directly address the measured failure. Each pass should be evaluated independently, followed by an end-to-end evaluation confirming that consolidation preserves recall and does not introduce duplicated findings.

質問 # 84
Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
B. Proceed with additional lookups without modifying the existing tool output context
C. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
D. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues

正解：A

解説：
Keeping only the return-relevant fields preserves the information needed for the current task while reducing unnecessary context load. This improves reliability and efficiency before adding more order lookups, because the agent retains structured, task-specific details without carrying verbose tool outputs that are no longer useful.

質問 # 85
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