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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q162-Q167):

質問 # 162
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.
An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

A. Start fresh and re-read the same files
B. Use --resume auth-deep-dive to load that specific session by name
C. Use --continue to pick up where the most recent conversation left off
D. Use --session-id with the UUID from yesterday's session transcript file

正解：B

解説：
Claude Code supports resuming a specific saved session by either its identifier or its assigned name. Because the engineer knows the target session is named auth-deep-dive , the appropriate command is:
claude --resume auth-deep-dive
Anthropic's CLI reference explicitly states that --resume can resume a session by ID or name and gives named-session usage in the same form. Resuming restores the relevant conversation history and accumulated context, including the prior analysis and files discussed during the investigation. ( https://docs.anthropic.com
/en/docs/claude-code/cli-reference )
Option B is incorrect because --continue resumes the most recent session in the current directory. The engineer has conducted three subsequent sessions, so it may open an unrelated investigation. Option A uses a flag that is not the documented Claude Code mechanism for selecting a prior session; --resume itself accepts the session identifier when an ID is used. Option C discards two hours of established context and unnecessarily repeats codebase exploration.
Named sessions are particularly useful when engineers alternate among multiple projects or parallel investigations. Assigning descriptive names allows the correct thread to be retrieved deterministically rather than depending on chronological recency.
Official references/topics: Claude Code Session Persistence, Named Sessions, --resume , --continue .

質問 # 163
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues:
(1) the subagent reports "insufficient results" instead of trying
alternative approaches when the specified searches fail,
(2) research quality drops for emerging topics that do not match
expected patterns, and
(3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?

A. Add fallback directives requiring alternative query formulations whenever the specified searches produce fewer than a predetermined number of results.
B. Classify each topic as either "well-defined" or "exploratory" and use a different instruction style for each category.
C. Remove procedural details entirely and delegate using simple goals such as "research this topic thoroughly," relying on the subagent's general capabilities.
D. Specify research objectives and quality criteria--such as coverage breadth, source diversity, and recency--rather than prescribing procedural steps, allowing the subagent to determine its search strategy.

正解：D

解説：
Option A establishes a clear destination without forcing the subagent to follow a brittle route. The prompt should define the research question, boundaries, expected deliverable, acceptable source types, recency requirements, coverage expectations, and verification criteria. The subagent can then broaden, narrow, reformulate, or follow emerging leads according to observed search results.
Anthropic's research-agent guidance emphasizes giving each subagent an objective, output format, source guidance, and clear task boundaries. It also describes research as dynamic and path-dependent, requiring agents to adapt their strategy as new information emerges.

質問 # 164
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. Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.
C. Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
D. Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.

正解：C

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 # 165
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 invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation-for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
B. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.
C. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
D. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.

正解：D

解説：
Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback. Option A is unsafe because automatically recalculating a total may overwrite the amount actually printed on the invoice; a mismatch can originate in an OCR or line-item extraction error. Option C repeats the same extraction without explaining what failed, wasting calls and relying on chance. Option D may prevent some format violations, but JSON Schema cannot express every cross-field arithmetic relationship or external vendor-registry rule. A validator-guided second pass therefore reduces manual review without silently changing source facts, while keeping deterministic code-not the model-as the final acceptance authority.

質問 # 166
Which practice MOST improves prompt maintainability?

A. One paragraph containing all instructions.
B. Minimal punctuation.
C. Random ordering.
D. Clearly separated sections with headings.

正解：D

解説：
Organized prompts with labeled sections improve readability for both developers and Claude.
Clear structure reduces ambiguity, simplifies maintenance, and makes future prompt modifications significantly easier.

質問 # 167
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