今あなたが無料でTopexamが提供したAnthropicのCCDV-F認定試験の学習ガイドをダウンロードできます。それは受験者にとって重要な情報です。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Streaming and Batch API - Vision capabilities - Claude Messages API - SDK and third-party integration |
| Topic 2: Prompt and Context Engineering | 11% | - Context window management - Structured output handling - Prompt design and structuring |
| Topic 3: Model Selection and Optimization | 16.8% | - Cost and token optimization - Latency and performance trade-offs - Claude model family characteristics |
| Topic 4: Agents and Workflows | 14.7% | - Memory and context management - Claude Agent SDK usage - Workflow vs autonomous agents - Agent architecture principles |
| Topic 5: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 6: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 7: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 8: Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
実際、私たちはCCDV-F試験参考書に関するサービスとお客様に対する忠実がずっと続いています。だから、CCDV-F試験参考書に関連して、何か質問がありましたら、遠慮無く私たちとご連絡致します。私たちのサービスは２４時間で、短い時間で回答できます。 私たちのCCDV-F試験参考書は、あなたがCCDV-F試験に合格する前に最高のサービスを提供することを保証します。 これは確かに大きなチャンスです。絶対見逃さないです。
質問 # 29
A teammate has submitted a pull request that adds a Claude-powered feature to your service. The code works, but the prompt and model selection are hard-coded inline, error handling is missing, and there are no tests for the integration.
What would you request during code review?
正解：B
解説：
D is the only option that addresses all three identified production-readiness defects. The supplied Claude Developer item explicitly selects D. A functioning happy path is insufficient for a maintainable Claude integration.
Prompt and model selection are configuration concerns that will change as prompts are evaluated, model versions evolve, or environments require different behavior. They should therefore be separated from unrelated business logic rather than scattered as inline constants. Claude API failures must also be handled deliberately. Anthropic documents typed SDK exceptions and defined HTTP error categories, including invalid requests, authentication failures, rate limits, server errors, overload, and timeouts. The official SDKs additionally retry appropriate transient failures.
Tests are required to verify the integration boundary, including successful behavior, malformed or unexpected responses, API error handling, and critical user workflows. Approving code without those controls pushes known reliability debt directly into production.
A and B knowingly merge incomplete production behavior. C improves configuration and test coverage but leaves a known API-failure path unhandled.
Relevant Claude Developer topics: SW Eng Foundations, code review, separation of configuration, error handling, integration testing, API resilience, maintainability, and production readiness .
質問 # 30
Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?
正解：B
解説：
Option B is correct because plugin dependencies and releases must be versioned deliberately if the team wants reproducible behavior and a reliable rollback path. Claude Code's plugin documentation supports explicit semantic versions in plugin manifests and marketplace entries, and the plugin system uses the resolved version as part of update detection and caching. Version constraints can also keep dependent plugins on a tested compatible range until the team intentionally upgrades.
The incident happened because the team could not determine which plugin version had previously worked.
Explicit tracking solves that directly: record the version in project configuration, pin known-good releases where stability matters, test upgrades, and change versions through reviewed configuration updates. This creates traceability and makes regression isolation much faster.
Option A removes useful extension functionality instead of managing it. Option C treats third-party change as uncontrollable even though the platform provides version-management mechanisms. Option D automatically moving everything to the latest release increases change frequency and can reproduce the same regression problem.
Therefore, B is the correct configuration-management response. Relevant Study Guide topics: Claude Code plugins, semantic versioning, dependency constraints, reproducible configuration, controlled upgrades, rollback, and configuration management.
質問 # 31
Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?
正解：B
解説：
Option A correctly applies selective enforcement. Claude Code hooks can execute automatically at lifecycle events such as PreToolUse, and matchers or conditions can narrow exactly which operations trigger a hook.
Anthropic's hook reference demonstrates this pattern by applying a PreToolUse hook specifically to destructive shell operations rather than indiscriminately processing every command. The documentation notes that if the matcher or conditional expression does not match, the handler is skipped, avoiding unnecessary process-spawn overhead.
That architecture is particularly appropriate for costly or security-sensitive checks. High-risk events- destructive file operations, privileged commands, production changes, or access to sensitive resources-can receive deterministic pre-execution enforcement while routine low-risk actions proceed without additional hook latency.
B creates an avoidable period in which safeguards disappear entirely. C reduces application availability without addressing the actual source of overhead. D is technically weaker because system-prompt instructions influence model behavior but are not equivalent to deterministic lifecycle interception capable of blocking execution.
Therefore, hooks should be scoped using event types, matchers, and conditions according to risk. Relevant Claude Developer topics are Claude Code hooks, agent construction, tool governance, deterministic controls, permission boundaries, safety/performance tradeoffs, and lifecycle interception.
質問 # 32
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?
正解：B
解説：
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.
質問 # 33
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?
正解：B
解説：
Option C correctly identifies threat categories introduced or significantly amplified by LLM-based application architecture. Prompt injection attempts to manipulate Claude into following adversarial instructions.
Jailbreaks seek to circumvent behavioral or application safeguards. Data leakage can expose confidential information contained in system prompts, retrieved context, tool results, or conversation state. Unsafe model output becomes particularly serious when output is consumed by downstream systems or translated into tool actions.
Anthropic's official guardrail documentation explicitly distinguishes jailbreaks and direct prompt injection from indirect prompt injection. It recommends input screening, hardened system prompts, structured handling of untrusted tool content, least-privilege access, output screening, and continuous monitoring. Anthropic also warns that sensitive prompt/context data can leak and recommends post-processing and output filtering where required.
Traditional threats such as SQL injection or XSS remain relevant to the surrounding application, but they are not the additional AI-specific categories requested. B similarly describes conventional infrastructure threats.
D is far too narrow because the SDK is only one component of the attack surface.
The supplied exam source explicitly marks C. Relevant topics: Claude App Design, threat modeling, prompt injection, jailbreaks, data leakage, output safety, least privilege, and defense in depth.
質問 # 34
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