CCDV-Fテストガイドは、時間の無駄を避けるために、できるだけ早くこれらの資料を学習できることを保証できます。 Claude Certified Developer-Foundations Study Questionは、不明瞭な概念を簡素化することにより、学習方法を最適化するのに役立ちます。 CCDV-F試験問題は、アフターサービスを完璧にするための努力をspareしみません。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Topic 2: Applications and Integration | 33.1% | - Vision capabilities - SDK and third-party integration - Streaming and Batch API - Claude Messages API |
| Topic 3: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 4: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 5: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 6: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 7: Agents and Workflows | 14.7% | - Agent architecture principles - Workflow vs autonomous agents - Claude Agent SDK usage - Memory and context management |
| Topic 8: Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
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質問 # 23
Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?
正解：C
解説：
Option B is correct because Claude API cost is fundamentally driven by usage volume multiplied by the token economics of the selected model and features. A credible projection must therefore estimate requests, average input tokens per request, average output tokens per request, applicable input/output prices, and expected prompt-cache behavior. Anthropic's pricing documentation treats input, output, cache writes, and cache reads as distinct billable categories, with cache hits priced below standard input processing.
A practical forecast can be modeled as: request volume × expected per-request input cost plus request volume
× expected per-request output cost, adjusted for cache-write/read rates and any other applicable pricing modifiers. The model should also include ranges for variance rather than only one point estimate, because token lengths and cache-hit rates will fluctuate in production.
Option A assumes a historical application has the same token profile and pricing, which may be false. Option C omits output tokens even though output pricing can be a material share of cost. Option D postpones the very token estimates the product team needs before launch.
Therefore, B is the complete projection method. Relevant Study Guide topics: token accounting, model pricing, prompt caching, cost forecasting, workload sizing, and production economics.
質問 # 24
You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...
正解：C
解説：
Production deployment does not terminate the software lifecycle. Once a Claude application is actively serving users, it enters an operations and maintenance phase in which developers and operators monitor performance, evaluate model behavior, respond to failures, control costs, manage security, and evolve the implementation as requirements or model capabilities change.
Anthropic's official developer documentation explicitly separates the journey into build, evaluate-and-ship, and operate stages. The operating stage includes workspace administration, API-key management, usage monitoring, and model migration. This confirms that production deployment is a transition into ongoing operation rather than the endpoint of development.
For LLM applications, maintenance is especially important because production traffic can expose input distributions and failure modes that were not fully represented during pre-release evaluation. Operational data should feed back into evaluations, prompt improvements, guardrails, architecture decisions, and model- version planning.
A incorrectly treats deployment as final. B may be useful but is only one governance activity, not the overall lifecycle phase. D assumes a mandatory organizational separation that is neither required nor generally desirable.
The supplied exam source marks C. Relevant topics: Systems Life Cycle, production operations, monitoring, maintenance, incident response, evaluation, and continuous system evolution.
質問 # 25
Your Claude application's content policy specifies categories of content it should not produce under any circumstance. The application currently has no mechanism to enforce this policy, and content matching these categories is appearing in the application's output.
How would you enforce the content policy?
正解：C
解説：
Option D is the strongest enforcement design because an unconditional content policy requires an application- level control between model generation and user delivery. Prompt instructions are valuable for steering Claude, but they are probabilistic controls and should not be treated as the sole enforcement mechanism when prohibited categories must never be exposed.
Anthropic's guardrail guidance recommends layered safeguards including screening, validation, monitoring, and filtering rather than relying exclusively on prompts. Its prompt-leak guidance specifically recommends output screening and post-processing, including deterministic techniques such as keyword matching, regular expressions, or other text-processing mechanisms where appropriate.
A improves the probability of policy compliance but cannot guarantee that every generated response will satisfy an externally defined application policy. B explicitly abandons the requirement. C detects violations only after exposure, which is unsuitable when the content must not reach users.
A production architecture can combine system instructions, structured classification, policy engines, deterministic rules, and model-based moderation, but the decisive requirement is enforcement before output delivery. Relevant Claude Developer topics are guardrails, output filtering, content moderation, deterministic enforcement, defense in depth, safe application boundaries, and production Claude application design.
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質問 # 26
You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?
正解：A
解説：
Option B follows standard secrets-management practice and Anthropic's explicit guidance for API credentials.
API keys are authentication secrets and should not be embedded in source code or committed to repositories.
Anthropic's authentication documentation explicitly recommends storing API keys in a secrets manager, rotating them periodically, and revoking credentials suspected of compromise. Anthropic SDKs can load Claude credentials from environment variables such as ANTHROPIC_API_KEY, allowing the secret to be injected at runtime instead of compiled into the application.
The same principle applies to external service credentials used by a Claude application. Development, staging, and production should normally receive independently scoped credentials through the deployment environment or secret-management infrastructure.
A creates a high-probability credential leak because repository history can retain secrets even after a later deletion. C violates isolation and least privilege by sharing credentials across services and users. D uses email as an uncontrolled secret-distribution channel and creates inconsistent manual configuration.
Therefore, B supplies credentials only at runtime while keeping them outside source control and enabling rotation and environment-specific access. Relevant Study Guide topics: API-key management, secrets managers, environment configuration, credential rotation, least privilege, and secure configuration.
質問 # 27
A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?
正解：C
解説：
Option B is correct. A large prompt containing overlapping, contradictory, or obsolete instructions creates instruction ambiguity and increases the likelihood that Claude will apply an unintended rule, over-weight irrelevant guidance, or produce inconsistent behavior.
Anthropic's current prompting guidance reinforces this principle: Claude performs best when instructions are clear, explicit, and direct. Prompt design should minimize ambiguity and provide the model with precisely the context and behavioral requirements needed for the task. Refactoring should therefore remove obsolete requirements, consolidate duplicated instructions, resolve contradictions, and establish one authoritative formulation for each behavioral rule. The revised prompt should then be regression-tested against representative inputs.
A makes an already overloaded prompt larger and relies on instruction ordering instead of eliminating conflicts. C addresses only one defect while retaining redundant and obsolete material. D does not inherently solve contradictions and may fragment authoritative instructions across multiple locations.
Therefore, B addresses the root cause. Relevant Study Guide topics: system prompt design, prompt maintainability, instruction clarity, conflicting instructions, prompt refactoring, regression evaluation, and context quality.
質問 # 28
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