CCDV-F学習教材の練習試験や模擬試験はみんなにとって重要です。今のリビジョン条件はCCDV-F試験に参加する良い機会です。したがって、レビュープランを調整するために、CCDV-Fの各練習問題を要約することが不可欠です。今、私たちはCCDV-F実際試験を模擬するためにオンラインテストエンジンとWindowsソフトウェアを追加しました。
| Section | Weight | Objectives |
|---|---|---|
| Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Eval, Testing, and Debugging | 2.6% | - Testing and Debugging
|
| Security and Safety | 8.1% | - Secure Application Design
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
Fast2testのCCDV-F資料を言及するたびに、多くの人の反応は高い出題率です。Anthropic認証に参加する人が不安の状態から平静になって、試験に順調に合格しました。新しい資料がないなら、努力だけが不足です。CCDV-F試験に合格したいなら、我々の全面的な資料を参考として試験を準備しましょう。
質問 # 79
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
正解：A
解説：
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
質問 # 80
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?
正解：A
解説：
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.
質問 # 81
You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?
正解：C
解説：
Option C follows the governing constraint in the scenario: data residency is an enterprise requirement, whereas minimizing infrastructure management is a preference. A deployment architecture cannot knowingly violate a mandatory compliance boundary merely to accelerate delivery. The question should therefore be interpreted under its stated assumption that the described BYOC environment satisfies the enterprise residency policy.
Anthropic's current Managed Agents documentation distinguishes Anthropic-managed cloud environments from self-hosted sandboxes running on infrastructure controlled by the customer. Self-hosting keeps tool execution, files, and network egress within customer infrastructure, although Anthropic documents that model- facing tool inputs and outputs can still pass through its control plane; therefore, actual regulatory approval must be based on the organization's precise data-flow requirements.
B and D explicitly defer or violate the existing policy. A unnecessarily introduces a planned migration away from the residency-compliant architecture without establishing that such a migration would be permissible.
Accordingly, C is the intended certification answer. Relevant Study Guide topics: deployment topology, data residency, self-hosting, compliance constraints, infrastructure ownership, security boundaries, and architecture tradeoffs.
質問 # 82
Your Claude application processes 50-page legal contracts and produces summaries with citation references back to the source. The team is debating whether to send each contract whole or split it into smaller pieces.
The contracts fit within Claude's context window. Initial testing shows that whole-document processing produces summaries with stronger cross-section reasoning but occasionally drifts on citation accuracy in later sections. Chunked processing produces stronger citation accuracy per chunk but loses cross-section reasoning.
The team has not decided which property matters more.
How would you guide the team's decision?
正解：B
解説：
B is correct because the architecture cannot be chosen intelligently until the team defines which quality attribute is more important to the actual business use case . The examination source explicitly selects B.
Whole-document and chunked processing each perform better on different dimensions, so there is no universally superior option.
Anthropic's evaluation guidance emphasizes defining concrete success criteria first and validating candidate approaches against representative examples. The correct sequence is therefore to determine whether cross- section reasoning or citation precision is the more critical requirement, establish measurable acceptance thresholds, and evaluate both strategies on representative legal contracts.
Anthropic's legal summarization guidance also confirms that chunking or meta-summarization is useful for long documents, while acknowledging that processing an entire document can be appropriate when it fits within Claude's context window. For citations specifically, Anthropic provides native citation support that chunks document text into citation-addressable units and improves source-grounded citation behavior.
A and C each privilege one quality dimension before establishing business priority. D focuses on cost and latency even though the unresolved problem is quality tradeoff.
Relevant Claude Developer topics: Understanding Reqs, success criteria, requirement prioritization, long- context processing, chunking, citation accuracy, cross-document reasoning, representative evaluations, and architecture tradeoffs .
質問 # 83
You are designing a multi-step Claude workflow where some steps must reason without seeing the full prior conversation history. The team wants to keep specific context isolated to specific steps.
The context engineering technique you would use is...
正解：A
解説：
Option C is the correct application of context isolation. A specialized step should receive the minimum relevant information required for its own task rather than inheriting an ever-growing global transcript. This improves signal-to-noise ratio, limits accidental cross-task influence, controls token usage, and makes individual components easier to evaluate.
Anthropic's context-engineering guidance explicitly identifies multi-agent architectures as a technique for long-horizon work. Specialized subagents can operate with their own context windows and return condensed results to an orchestrating agent rather than exposing every agent to every intermediate detail. This architecture protects each reasoning process from irrelevant history while allowing the overall system to preserve necessary state.
Options A and D represent the opposite approach: indiscriminately loading the full prior history. Larger context is not automatically better; Anthropic warns that excessive context can introduce context pollution and degrade retrieval or attention to important information. B also fails because a single global prompt does not isolate state or tailor the information available to each processing stage.
Therefore, C best implements scoped reasoning boundaries. Relevant Claude Developer topics are multi-agent architecture, subagents, context isolation, context engineering, orchestration, and long-running workflow design.
質問 # 84
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