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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - SDK and third-party integration - Claude Messages API - Vision capabilities - Streaming and Batch API |
| Topic 2: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 3: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 4: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 5: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 6: Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
| Topic 7: Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Topic 8: Agents and Workflows | 14.7% | - Claude Agent SDK usage - Workflow vs autonomous agents - Memory and context management - Agent architecture principles |
あなたはいまAnthropicのCCDV-F認定試験にどうやって合格できるかということで首を傾けているのですか。AnthropicのCCDV-F認定試験は現在のいろいろなIT認定試験における最も価値のある資格の一つです。ここ数十年間では、インターネット・テクノロジーは世界中の人々の注目を集めているのです。それがもう現代生活の不可欠な一部となりました。その中で、Anthropicの認証資格は広範な国際的な認可を得ました。ですから、IT業界で仕事している皆さんはAnthropicの認定試験を受験して資格を取得することを通して、彼らの知識やスキルを向上させます。CCDV-F認定試験はAnthropicの最も重要な試験の一つです。この資格は皆さんに大きな利益をもたらすことができます。
質問 # 24
You are choosing between using STDIO-based communication and HTTP-based communication for an MCP server. The server will be invoked by a Claude Code session running locally.
Which communication pattern would you use?
正解：B
解説：
Option D is directly supported by both the supplied examination material and Claude Code's MCP documentation. The examination source selects STDIO for this local-process scenario.
Claude Code's official MCP guidance distinguishes remote HTTP servers from local STDIO servers .
Anthropic states that STDIO servers run as local processes and communicate through standard input and standard output. They are particularly suitable for local tools requiring direct machine access or for custom scripts. Claude Code can launch the process itself and communicate with it without creating, exposing, securing, or maintaining a network listener.
HTTP is appropriate when the MCP server exists as a separately reachable network service, especially for cloud-hosted or shared services. That requirement is absent here: the server is being invoked locally from a Claude Code session.
A creates duplicate transport infrastructure without a stated availability requirement. B incorrectly assumes one transport is universally superior regardless of deployment topology. C describes HTTP polling rather than the normal MCP transport relationship and introduces unnecessary request overhead.
Therefore, local execution strongly favors STDIO; remote/shared deployment generally favors HTTP.
Relevant Claude Developer topics: MCP architecture, STDIO transport, HTTP transport, Claude Code integrations, local process communication, and deployment topology .
質問 # 25
You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...
正解：A
解説：
The supplied Claude Certified Developer Foundations source marks C . Model selection is a multidimensional engineering decision. There is no universally correct Claude model independent of workload requirements; the application must satisfy the required capability or quality while remaining within acceptable latency and cost envelopes.
Anthropic's official model-selection guidance explicitly identifies capabilities, speed, and cost as core considerations and recommends testing models against workload-specific benchmarks rather than selecting them from a single metric. The guidance further recommends evaluating actual prompts and data, comparing response accuracy, quality, and edge-case behavior, and then weighing the resulting performance and cost tradeoffs.
Options A, B, and D each establish one or two dimensions as primary and effectively defer the remainder.
That can lead to a technically unsuitable model-for example, a cheap model that fails the quality threshold or a high-quality model whose latency makes the user experience unacceptable.
The correct method is to define minimum acceptable thresholds across all relevant dimensions and benchmark candidate models against the actual workload.
Relevant Claude Developer topics: Claude App Design, model selection, capability, quality, latency, cost, benchmarking, workload evaluation, tradeoff analysis, and production optimization .
質問 # 26
You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?
正解：B
解説：
A is correct because the workflow is state-dependent, non-linear, and iterative . The source explicitly identifies the graph-based pattern as the intended architecture for this scenario. Each stage-fact extraction, eligibility evaluation, and decision-making-has its own completion criteria, and the process may need to move backward from eligibility evaluation to fact extraction when information is incomplete. A graph representation naturally models these conditional transitions and loops.
Anthropic's current orchestration guidance supports workflows containing branching, loops, filtering, staged execution, and state-dependent control flow , rather than forcing every task through one fixed sequence.
Dynamic workflow orchestration can use explicit control logic so the next processing stage depends on current state and previous results.
B concerns progressive output delivery, not workflow-state transitions. C provides a generic agentic tool loop but does not explicitly model distinct states or transition criteria. D is unsuitable because it prohibits the required return path between extraction and eligibility evaluation.
Therefore, a graph-based architecture provides the necessary conditional routing, iteration, and stage-specific validation.
Relevant Claude Developer topics: Agent Patterns, graph workflows, state transitions, conditional branching, loops, stage-specific success criteria, and agent orchestration .
質問 # 27
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?
正解：C
解説：
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .
質問 # 28
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?
正解：C
解説：
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .
質問 # 29
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世界経済の急速な発展に伴い、ますます多くの人々が社会的エリートになることを切望していることが広く受け入れられています。 CCDV-F最新の学習ガイド資料は、ソーシャルエリートになりたい多くの人々の近道となります。 CCDV-F試験の準備に最善を尽くし、短時間で関連する認定を取得すれば、私たちのような大企業の多くのリーダーから注目を集めることが容易になり、非常に簡単になります。 CCDV-F学習ガイドの助けを借りて、多くの人々が労働市場で適切な仕事を得ることができます。
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