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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Safety | 8.1% | - Safety and Guardrails
|
| Topic 2: Model Selection and Optimization | 16.8% | - Performance and Cost Optimization
|
| Topic 3: Tools and MCPs | 10.6% | - Model Context Protocol
|
| Topic 4: Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Topic 6: Applications and Integration | 33.1% | - Application Development and Integration
|
| Topic 7: Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Topic 8: Claude Code | 3.1% | - Claude Code Configuration and Usage
|
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39. Frage
A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?
Antwort: B
Begründung:
The supplied Claude Certified Developer Foundations question identifies D as the correct answer. Anthropic prices input and output tokens separately, so application cost models must independently account for the number of tokens supplied to Claude and the number generated in response.
Anthropic's official pricing documentation confirms that output-token pricing is normally higher than base input-token pricing. For example, current Claude models maintain distinct columns for base input tokens and output tokens; the exact dollar amounts depend on the selected model. This distinction matters because an application with relatively small prompts but very large generated responses can incur substantial output- token expenditure even when input volume is modest.
Therefore, cost forecasting should use approximately: input tokens × input rate + output tokens × output rate , with additional adjustments where prompt caching, batch processing, server tools, or other pricing modifiers apply. Option A incorrectly assumes equivalent rates, B reverses the normal relationship, and C incorrectly excludes generated tokens from billing.
Relevant Claude Developer topics: Claude API Mechanics, token accounting, model pricing, input/output token usage, cost modeling, prompt caching, and production API economics .
40. Frage
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?
Antwort: D
Begründung:
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.
41. Frage
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?
Antwort: A
Begründung:
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.
42. Frage
You are building a Claude application that needs to maintain a persistent connection to a service that streams real-time updates. The team is unsure what communication pattern to use.
Which communication pattern would you use?
Antwort: D
Begründung:
Option B is the appropriate software-engineering communication pattern when the application requires a persistent, low-latency, bidirectional channel. WebSockets establish a connection using an HTTP Upgrade handshake and then maintain a TCP-based communication channel in which either side can send messages independently. This eliminates the repeated connection setup and request overhead associated with conventional polling.
RFC 6455 defines WebSocket specifically as a protocol enabling two-way communication and explains that it provides a single TCP connection as an alternative to HTTP polling for interactive communication.
Option A can work for infrequent updates, but repeatedly opening HTTP requests adds latency, headers, and server/client overhead and is unsuitable when continuous real-time communication is the stated requirement.
C resembles long polling or an ad-hoc streaming connection but lacks the standardized framing, lifecycle behavior, and interoperability provided by WebSocket. D introduces filesystem polling and is not an appropriate network-streaming architecture.
The important certification principle is selecting a communication mechanism based on application requirements rather than merely choosing an available protocol. For persistent two-way streaming, WebSocket provides the intended abstraction. Relevant Claude Developer topics are software engineering foundations, client-server communication, persistent connections, HTTP versus WebSocket patterns, streaming, and real-time application architecture.
43. Frage
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?
Antwort: B
Begründung:
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 .
44. Frage
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