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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code	3.1%	- Claude Code configuration and usage
Security and Safety	8.1%	- AI application security - Guardrails and safety controls
Tools and Model Context Protocol (MCP)	10.6%	- Tool integration and usage - MCP server development
Agents and Workflows	14.7%	- Memory and context management - Agent architecture principles - Workflow vs autonomous agents - Claude Agent SDK usage
Evaluation, Testing, and Debugging	2.6%	- Output evaluation and validation - Error handling and debugging
Prompt and Context Engineering	11%	- Context window management - Structured output handling - Prompt design and structuring
Applications and Integration	33.1%	- Streaming and Batch API - Vision capabilities - SDK and third-party integration - Claude Messages API
Model Selection and Optimization	16.8%	- Cost and token optimization - Latency and performance trade-offs - Claude model family characteristics

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Anthropic Claude Certified Developer-Foundations 認定 CCDV-F 試験問題 (Q54-Q59):

質問 # 54
You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
B. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
C. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
D. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.

正解：B

解説：
Option B is the best answer because the task requires deliberate multi-step reasoning over competing hypotheses before a final recommendation. Anthropic's thinking documentation explains that enabling thinking gives Claude additional reasoning capacity before producing the final response, and larger thinking budgets can improve performance on complex analytical tasks. That maps directly to a differential-diagnosis workflow involving evidence comparison, hypothesis elimination, and multi-stage synthesis.
Option A uses ordinary zero-shot prompting and does not specifically allocate additional reasoning capacity.
Option C explicitly optimizes for minimum latency at the expense of reasoning depth, which conflicts with the complexity requirement. Option D reduces model and context capacity and therefore has no principled relationship to better differential reasoning.
One current-platform nuance is important: manual "extended thinking" is the legacy configuration on supported Claude 4.5/4.6 models, while newer models use adaptive thinking. That does not change the question's underlying answer: among the listed choices, B is the only option that intentionally provides a reasoning phase for complex analysis. Relevant Study Guide topics: thinking, reasoning budgets, complex- task model configuration, latency-quality tradeoffs, and model selection.

質問 # 55
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?

A. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.
B. Run Claude API calls in a separate thread pool isolated from the rest of the service so the main event loop is not affected.
C. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.
D. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.

正解：A

解説：
Option C is the natural integration model for an application that already uses asynchronous I/O. Claude API calls are network-bound operations, so blocking the application's event loop while waiting for responses would reduce concurrency and impair throughput. The Anthropic Python SDK explicitly provides AsyncAnthropic, and its documented usage awaits client.messages.create() directly. It also supports an aiohttp backend when improved asynchronous concurrency is desired.
This means Claude requests can participate in the same cooperative asynchronous execution model as other database, HTTP, or service calls. While one request waits for remote I/O, the runtime can continue serving other work rather than dedicating the event loop to an idle blocking operation.
A can be used when integrating unavoidable blocking libraries into async software, but it adds thread-pool management when an official asynchronous client already exists. B needlessly redesigns a concurrent REST service around a synchronous integration. D is specifically harmful because blocking the event loop prevents normal concurrent request processing.
Therefore, C uses the abstraction provided for exactly this architecture. Relevant Study Guide topics: Claude SDK clients, asynchronous APIs, awaitable I/O, concurrency, REST-service integration, and scalable application architecture.

質問 # 56
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
B. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
C. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.
D. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.

正解：C

解説：
Option C accurately captures Anthropic's distinction. Prompt engineering primarily concerns how instructions are written, structured, and organized to obtain the desired behavior from a particular model invocation.
Techniques include explicit instructions, examples, roles, XML structure, output requirements, and task- specific prompt construction. Context engineering operates at a broader architectural level: it determines which information should actually be present in the model's context at each inference step.
Anthropic defines prompt engineering as methods for writing and organizing LLM instructions, whereas context engineering encompasses strategies for curating and maintaining the optimal set of tokens during inference. For long-running agents, context can contain system instructions, tools, MCP resources, retrieved documents, prior messages, tool results, summaries, and memory.
This distinction matters because multi-step agents continuously generate new state. Effective systems may prune obsolete results, retrieve information just in time, compact earlier conversation history, isolate subagent contexts, or store persistent state externally. B is incorrect because context engineering has not simply replaced prompt engineering; the two operate at different scopes. A defines context too narrowly, and D obscures an important architectural distinction.
Therefore, C correctly represents Claude Developer coverage of prompt engineering versus context engineering, context curation, agent state, long-horizon workflows, and context-window optimization.

質問 # 57
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
B. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
C. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
D. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.

正解：A

解説：
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.

質問 # 58
Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

A. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
B. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
C. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.
D. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.

正解：B

解説：
Option B establishes the correct trust boundary. Fluent or confident language is not evidence that a generated claim is factually correct. If downstream systems treat output as authoritative data, the application must independently establish whether the output meets its correctness requirements before accepting it.
Validation can take several forms depending on the workload: compare generated facts against authoritative records, require citations or source references, constrain output to retrieved evidence, apply deterministic business rules, or use separate evaluation/classification stages. Anthropic's agent engineering guidance repeatedly emphasizes explicit evaluation criteria and validation rather than relying on apparent confidence.
A confuses sampling behavior with factual reliability. Lowering temperature does not establish factual correctness and may only make an incorrect answer more repeatable. C supplies maximum oversight but is unnecessarily expensive and removes useful automation even for low-risk, easily validated cases. D communicates uncertainty to users but does not protect downstream systems that automatically consume the response.
Therefore, B treats model output as untrusted until verified to the level required by the application. Relevant Study Guide topics: output validation, grounding, factuality, confidence calibration, source verification, trust boundaries, and downstream safety.

質問 # 59
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