CCAR-F最新対策問題 & CCAR-F PDF

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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic CCAR-F PDF & CCAR-F合格体験談

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q148-Q153):

質問 # 148
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?

A. Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.
B. Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
C. Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
D. Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.

正解：A

解説：
Enforcing the policy outside of the model via a PreToolUse hook guarantees compliance. By intercepting high-value refund attempts and triggering human escalation, the system ensures the rule is applied consistently regardless of prompt instructions or agent behavior.

質問 # 149
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.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document- analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ?7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35?50 billion range.
What systematic approach best addresses this?

A. Instruct the synthesis agent to distinguish well-established findings from contested findings explicitly, preserving each source's original uncertainty, methodology, and supporting evidence.
B. Configure subagents to report only findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
C. Add a verification subagent that passes claims to synthesis only when they are corroborated by at least two independent sources.
D. Implement a confidence-calibration layer that normalizes subagent uncertainty expressions to probability scores between 0.0 and 1.0, and then calculate a confidence-weighted average.

正解：A

解説：
Option D preserves the evidence instead of manufacturing certainty. The two estimates are not directly interchangeable: one is an industry estimate with unspecified methodology, while the other is a peer-reviewed estimate with an explicit confidence interval. Converting both into model- generated confidence scores and calculating a weighted average would create a new figure that neither source reported and that may have no statistical validity. Anthropic's hallucination- reduction guidance recommends making claims auditable through quotations, citations, and supporting evidence rather than presenting unsupported synthesis as fact. Its Citations documentation similarly emphasizes retaining the exact source passages supporting individual claims. Filtering uncertain findings, option B, would remove decision-relevant information.
Requiring two-source corroboration, option C, could also discard credible evidence concerning emerging or specialized subjects. The synthesis agent should report the estimates separately, explain their methodological differences, identify which findings are strongly supported or disputed, and state what evidence would resolve the disagreement. This produces calibrated, useful reporting without arbitrary selection, excessive hedging, or false precision.

質問 # 150
Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?

A. Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
"old" should use archive_file.
B. Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.
C. Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.
D. Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.

正解：C

解説：
Clear tool descriptions directly guide the agent's choice between similar tools. Adding use-case guidance that backup files should not be deleted and should be archived helps the agent map
"remove old backups" to the correct archive operation before execution.

質問 # 151
After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

A. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
B. Add detailed few-shot examples demonstrating bug categories Claude should flag--race conditions, null dereferences, and error-handling gaps--while retaining the high-confidence filtering instruction.
C. Split the review into a finding stage whose objective is comprehensive coverage--reporting every potential issue with confidence and severity metadata--and a separate stage that verifies and thresholds those findings.
D. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.

正解：C

解説：
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.

質問 # 152
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

A. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
B. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
C. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
D. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.

正解：A

解説：
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular-expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema-constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true.
Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces.

質問 # 153
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