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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Prompt design strategies - Structured output generation and validation |
| Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns - Designing agentic systems and workflows |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
Anthropic CCAR-Fソフト版問題集のようなバーチャルは購入前に、どうすれば適用性を感じられますか。この問題に心配する必要がありませんし、我々社の無料に提供するAnthropic CCAR-FPDF版を直接にダウンロードし、事前に体験できます。何か問題があると、ライブチャットとメールで問い合わせます。我々MogiExamを選択するとき、Anthropic CCAR-F試験にうまく合格できるチャンスを捉えるといえます。
質問 # 37
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?
正解：C
解説：
Option C defines the reviewer's actual objective instead of relying on an undefined notion of confidence. The prompt should identify reportable categories such as correctness defects, security vulnerabilities, data-loss risks, broken requirements, and meaningful performance regressions. It should also specify that cosmetic preferences, lint-enforced formatting, speculative refactoring, and intentional repository conventions must not be reported unless they cause concrete incorrect behavior.
Anthropic's Claude Code best-practices guidance recommends telling a reviewer to report gaps affecting correctness or stated requirements and to treat other observations as optional. Without such criteria, a reviewer asked to "find issues" will predictably return technically defensible but operationally low-value observations.
質問 # 38
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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?
正解：D
解説：
Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata-such as document type, field, source format, and business risk-the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. ( https://docs.anthropic.com/en
/docs/build-with-claude/develop-tests ) A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
Option A detects only disagreements between stochastic extractions. Two attempts may produce the same plausible but incorrect answer, so agreement is not equivalent to factual correctness. Option B addresses known patterns but will miss new failure modes and may generate excessive false positives. Option C actually lowers the review threshold in the wrong direction: documents between 70% and 85% would be treated as automated rather than reviewed if the routing rule remains "below threshold," and changing thresholds alone does not measure high-confidence error prevalence.
Official references/topics: Evaluation Design; Representative Test Distributions; Continuous Reliability Measurement; Human-in-the-Loop Sampling.
質問 # 39
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 submits two requests:
* Request A: "Rename the getUserData function to fetchUserProfile everywhere it's used."
* Request B: "Improve error handling throughout the data processing module-add try/catch blocks, meaningful error messages, and ensure failures don't silently corrupt data." For which request does specifying an explicit multi-phase workflow (such as analyze # propose # implement with review) most improve outcome quality?
正解：B
解説：
Request B benefits substantially more from an explicit multi-phase workflow because it is broad, judgment- intensive, and likely to affect multiple execution paths. Improving error handling requires the agent to identify current failure modes, determine which exceptions should be caught, preserve useful diagnostic information, prevent partial state corruption, and verify that the resulting behavior remains consistent across the module.
An analyze # propose # implement # review sequence separates those concerns and creates checkpoints before consequential edits are made.
Request A is comparatively deterministic. The function and target name are already defined, and the task primarily requires locating references, applying the rename, and running tests or static analysis. Anthropic's Claude Code guidance states that planning is most useful when the approach is uncertain, the work spans multiple files, or the developer is unfamiliar with the affected code. For a small change that can be described as a straightforward diff, direct implementation is usually preferable. ( https://code.claude.com/docs/en/best- practices ) Anthropic also characterizes prompt chaining as appropriate when a task can be divided into fixed stages and additional latency is justified by increased accuracy. Request B fits that pattern because analysis and review materially reduce the risk of introducing swallowed exceptions or inconsistent recovery behavior. (
https://www.anthropic.com/research/building-effective-agents )
Official references/topics: Prompt Chaining; Plan Before Editing; Multi-Phase Coding Workflows; Verification and Review.
質問 # 40
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 agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?
正解：B
解説：
When Edit cannot identify a unique match, rewriting the fully read file is the most reliable way to control the exact insertion point. The other options are brittle, place the function incorrectly, or risk unintended replacements.
質問 # 41
Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?
正解：B
解説：
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.
質問 # 42
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CCAR-F試験準備資料は、同じ業界の製品よりも合格率が高くなっています。 CCAR-F認定に合格したい場合は、合格率の高い製品を選択する必要があります。 CCAR-F学習教材は、専門知識、サービス、柔軟なプラン設定から合格率を保証します。 99％の合格率は、CCAR-F学習教材の誇り高い結果です。最終的な目標はCCAR-F認定を取得することであるため、合格率も製品の選択の大きな基準であると考えています。
CCAR-F合格資料: https://www.mogiexam.com/CCAR-F-exam.html