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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development - Claude API and Client SDKs |
| Topic 2: Tools and MCPs | 10.6% | - Model Context Protocol - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas |
| Topic 3: Security and Safety | 8.1% | - Application Security - Safety and Responsible Development - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content |
| Topic 4: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 6: Agents and Workflows | 14.7% | - Agent Architecture - Agent Construction with Claude - Agent Patterns and Frameworks |
| Topic 7: Model Selection and Optimization | 16.8% | - Performance Optimization - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs |
| Topic 8: Prompt and Context Engineering | 11% | - Prompt Engineering - Context Management and Long-Context Techniques - Context Engineering |
私たちのCCDV-F試験問題は、最も重要で効果的な報酬は、あなたが試験に合格させ、CCDV-F認定試験資格書を得ることです。そしてそれは、すべての受験者が気になるものです。同時に、CCDV-Fでより実用的なスキルを得ることもでき、あなたの仕事の効率を向上させます。 私たちのCCDV-F試験問題は信頼に値する商品です。
質問 # 96
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?
正解：B
解説：
Option C correctly explains a fundamental property of generative language models. The supplied examination material identifies C as the correct answer. Claude generates subsequent tokens probabilistically rather than retrieving a single fixed answer for each prompt. Consequently, identical or highly similar requests can produce variations in wording, ordering, explanation depth, and sometimes substantive details.
Anthropic's API documentation explicitly states that sampling parameters control randomness and, importantly, that even configurations historically using a temperature of 0.0 were not fully deterministic .
Current newer Claude models increasingly manage sampling behavior internally, so application design should not assume byte-for-byte identical responses across executions.
Production systems therefore handle variation through architecture: schema validation for machine-consumed output, deterministic business-rule checks, retries where appropriate, evaluations for acceptable behavioral ranges, and application-level safeguards. Temperature adjustment may reduce variation on models that support the parameter, but it does not fundamentally convert an LLM into a deterministic function.
A incorrectly labels normal model behavior as a defect. B confuses network latency with generation variability. D overstates model snapshot behavior; pinning a model prevents silent model-version changes but does not eliminate sampling variability.
Relevant topics: sampling, nondeterminism, validation, retries, model versions, and robust Claude API integration .
質問 # 97
Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?
正解：D
解説：
Option D treats prompts as production configuration and executable application behavior rather than informal text. A prompt modification can change output format, tool-selection behavior, policy enforcement, downstream compatibility, token usage, or model quality just as materially as an ordinary source-code change. Consequently, prompt changes need traceability, review, testing, and rollback.
A single source of truth prevents different application paths from silently using inconsistent prompt versions.
Version control provides an immutable history showing who changed the prompt, what changed, and which application release used it. Pull-request review enables peer assessment and automated evaluation before deployment. The same configuration-as-code principle appears throughout Claude Code's project configuration model, where shared project configuration is designed to live with version-controlled project artifacts.
A removes governance and creates an uncontrolled external state. B increases hidden runtime behavior and bypasses normal review. C solves fragmentation but leaves the exact governance failure described in the scenario-the ability to ship prompt changes without review.
Therefore, D provides reproducibility and controlled change management. Relevant Study Guide topics:
prompt versioning, configuration management, source control, change review, reproducibility, regression testing, and rollback.
質問 # 98
You are designing a Claude application that processes user-submitted text. Some of that text could include sensitive information such as account numbers or passwords that the application should not send to Claude.
How would you design the application?
正解：D
解説：
Option A is correct because sensitive-data protection must occur before prohibited information crosses the application's trust boundary. If account numbers, passwords, or other secrets must not be sent to Claude, asking Claude to "ignore" those values after transmission does not satisfy the requirement. The application must determine which data is permitted to leave its controlled environment and transform or reject prohibited content before constructing the Claude request.
This principle is consistent with Anthropic's defense-in-depth guidance. Anthropic recommends input screening, limiting Claude's access to sensitive information, and applying least privilege so the model receives only information necessary for the task.
B is fundamentally too late: the sensitive information has already been transmitted to the model context before the system instruction can influence behavior. C is worse because logging can create an additional store containing the sensitive information and only detects exposure after it occurs. D implements some boundary filtering but deliberately leaves coverage incomplete until production incidents reveal additional patterns.
Therefore, A requires the boundary and its permitted data flows to be defined first, then enforced comprehensively through filtering, redaction, tokenization, or rejection. Relevant Study Guide topics:
application boundaries, data minimization, sensitive-data handling, filtering, redaction, least privilege, and privacy-preserving design.
質問 # 99
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 # 100
Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?
正解：C
解説：
Option B is the correct fault-tolerance pattern for a long-running stateful workflow. A checkpoint captures enough durable execution state-completed steps, intermediate results, pending work, identifiers, and other necessary task state-to restart from a known consistent point rather than replaying the entire workflow after interruption.
This pattern is consistent with Claude's current stateful agent architecture. Anthropic's Managed Agents documentation describes persistent sessions that preserve conversation history across interactions. When a session becomes idle, its sandbox can be checkpointed so filesystem and execution artifacts are available when work resumes. The certification concept is broader than that specific hosted implementation: long- running agent systems should externalize recoverable state at meaningful boundaries.
A longer timeout does not protect against process crashes, infrastructure restarts, network failures, or deployment interruptions. C doubles resource consumption and creates consistency problems without providing deterministic recovery. D wastes completed work and may repeat external side effects.
Therefore, B provides controlled resumability and minimizes repeated processing. Relevant Study Guide topics: checkpointing, persistent state, resumable workflows, long-running agents, fault tolerance, idempotency, and recovery architecture.
質問 # 101
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CCDV-F日本語独学書籍: https://www.passtest.jp/Anthropic/CCDV-F-shiken.html