Anthropic CCAR-F Exam | CCAR-Fクラムメディア -更新ダウンロードCCAR-F試験問題集

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code Configuration & Workflows	20%	- Claude Code 1. Code generation and automation 2. Configuration and project setup 3. Agent skills 4. Development workflows
Tool Design & MCP Integration	18%	- Tool integration 1. Model Context Protocol (MCP) 2. Tool selection and safety 3. Tool interface design 4. Resource and server integration
Prompt Engineering & Structured Output	20%	- Prompt design 1. Prompt engineering techniques 2. Output validation 3. Structured output and JSON schemas 4. Few-shot prompting
Context Management & Reliability	15%	- Context handling 1. Reliability and evaluation 2. Memory strategies 3. Cost and performance optimization 4. Context window management
Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Workflow design 2. Agent orchestration 3. Single-agent and multi-agent architectures 4. Planning and execution strategies

>> CCAR-Fクラムメディア <<

CCAR-F試験問題集、CCAR-F認定資格試験

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q34-Q39):

質問 # 34
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?

A. Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.
B. Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.
C. Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.
D. Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.

正解：D

解説：
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
Option A improves proximity between implementation and tests but does not tell Claude what coverage relationship to inspect. Option C could work, but it adds orchestration, latency, cost, and another handoff before testing whether a direct instruction solves the observed failure. Option D may improve recognition of examples resembling the demonstration, but a few cases cannot enumerate every branch structure. Explicit path enumeration generalizes across unfamiliar code and creates auditable output: each reported gap can name the uncovered condition, expected behavior, and missing assertion.

質問 # 35
After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
B. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
C. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
D. Pass the full accumulated context from all prior agents.

正解：B

解説：
Passing the synthesis draft with a structured source index gives the report generation agent enough information to produce a complete final report while avoiding the cost and noise of full raw context. Mapping key claims to source URLs and relevant excerpts preserves attribution, supports accurate citations, and keeps the final generation step efficient.

質問 # 36
Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
B. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
C. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
D. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.

正解：C

解説：
Structured error output gives the agent enough information to respond usefully. Including the error type, whether the issue is recoverable, and actionable guidance allows the agent to distinguish malformed tracking IDs, missing shipments, and temporary API failures, then suggest the correct next step to the user.

質問 # 37
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 extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals.
During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total-sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A. Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
B. Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.
C. Add a "calculated_total" field where the model sums extracted line items alongside a "stated_total" field. Flag records for human review when values differ.
D. Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.

正解：C

解説：
The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.
This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic's evaluation guidance recommends automated, code-based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total . The essential design principle remains the same: preserve stated_total , compute an independent total, compare them, and route discrepancies for adjudication.
Option A might improve behavior but cannot resolve genuine OCR corruption and could encourage the model to modify extracted values merely to create mathematical consistency. Option B introduces a second probabilistic judgment without new evidence. Option C is unacceptable for accounting data because it fabricates adjusted amounts and destroys fidelity to the invoice.
The schema should therefore expose both values and attach a validation status or review reason when they differ.
Official references/topics: Deterministic Validation; Human-in-the-Loop Review; Structured Output Design; Source-Fidelity Controls.

質問 # 38
A user expands the research system beyond its original web-search agent by adding specialized data sources.
A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news- monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality?

A. Add a format-conversion layer that transforms every subagent result into a common intermediate representation before synthesis.
B. Standardize all subagent outputs as JSON containing fields for claim, evidence, source, and confidence.
C. Standardize all subagent outputs as prose summaries with inline citations.
D. Update the synthesis agent to render each content type appropriately-financial data as tables, news as prose, and technology areas as structured lists.

正解：D

解説：
Option C preserves the semantic strengths of each information type instead of forcing heterogeneous evidence into one presentation structure. Financial values share comparable fields and therefore belong in a table. News developments require connected prose to preserve chronology and causality. Patent technology areas are naturally represented as grouped or prioritized lists.
Anthropic's prompting best practices recommend giving explicit output-format instructions and matching the requested format to the intended communication goal. The synthesis prompt should therefore include rendering rules for each recognized content type while maintaining common provenance fields behind the presentation.
Option A damages numerical comparability and makes trends harder to scan. Option B can be useful as an internal exchange schema, but presenting the entire briefing as uniform JSON does not produce an effective executive document. Option D normalizes transport but does not correct the synthesis agent's bullet-only rendering policy; a common representation can still be displayed badly. The correct design separates structured internal interchange from audience-facing presentation. The synthesis agent should preserve citations and metadata while selecting the most legible form for each class of evidence.

質問 # 39
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CCAR-F認定試験はたいへん難しい試験ですね。しかし、難しい試験といっても、試験を申し込んで受験する人が多くいます。なぜかと言うと、もちろんCCAR-F認定試験がとても大切な試験ですから。IT職員の皆さんにとって、この試験のCCAR-F認証資格を持っていないならちょっと大変ですね。この認証資格はあなたの仕事にたくさんのメリットを与えられ、あなたの昇進にも助けになることができます。とにかく、これは皆さんのキャリアに大きな影響をもたらせる試験です。こんなに重要な試験ですから、あなたも受験したいでしょう。

CCAR-F試験問題集: https://www.topexam.jp/CCAR-F_shiken.html