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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Agents and Workflows | 14.7% | - Claude Agent SDK usage - Agent architecture principles - Workflow vs autonomous agents - Memory and context management |
| Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - Streaming and Batch API - SDK and third-party integration |
| Prompt and Context Engineering | 11% | - Prompt design and structuring - Structured output handling - Context window management |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
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76. Frage
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?
Antwort: B
Begründung:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.
77. Frage
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?
Antwort: C
Begründung:
Option C applies an orchestrator-worker architecture to a request containing several distinct domains. Rather than making one agent continuously switch between billing, shipping, and product reasoning, an orchestrator can decompose the ticket, delegate each concern to an appropriately scoped specialist, track completion, and consolidate the resulting recommendations.
Anthropic describes this architecture directly: an orchestrator dynamically breaks down a task, delegates subtasks to worker agents, and synthesizes their results. Anthropic's multi-agent Research system similarly uses a lead agent that coordinates specialized subagents operating with independent contexts.
A rigid workflow is inappropriate because not every ticket contains the same combination or ordering of issues. B can improve behavior but leaves one agent responsible for managing all competing concerns and state. D increases raw context capacity without addressing decomposition or responsibility boundaries.
C is therefore the strongest architectural change when separate issue categories can be handled independently and then reconciled by a coordinating component. Relevant Study Guide topics: orchestrator-workers, subagents, delegation, task decomposition, context isolation, coordination, and synthesis.
78. Frage
You are establishing the guardrail strategy for a Claude application. The team wants to ensure guardrail failure does not expose the application to unsafe behavior.
The guardrail strategy would...
Antwort: C
Begründung:
Option A is correct because the safest guardrail architecture is defense in depth, not dependence on one control. Anthropic's guardrail guidance explicitly recommends combining multiple safeguards: input screening and validation, hardened system instructions, safe handling of untrusted tool content, least-privilege permissions, output screening, monitoring, and red-team testing. The important engineering property is independence: if one layer misses an attack or unsafe request, another layer can still prevent harmful behavior or block delivery.
Option B places control only at the output boundary. Human review can be useful for high-risk cases, but it does not protect tool execution, data access, prompt injection, or other failures that can occur before final output. Option C relies on a single system-prompt control; system instructions are probabilistic and cannot provide complete enforcement against adversarial or malformed inputs. Option D is weaker still because model-level safety alone does not enforce application-specific policies.
Therefore, A best matches Claude Developer security guidance: layer preventive, detective, and enforcement controls so there is no single guardrail whose failure exposes the application. Relevant Study Guide topics:
guardrails, prompt injection, input validation, output screening, least privilege, defense in depth, and production monitoring.
79. Frage
You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?
Antwort: D
Begründung:
Option B is the correct automation pattern. A CI pipeline must operate non-interactively, so Claude Code should be invoked in its programmatic/headless execution mode and given only the permissions required for the predefined workflow. Output can then be captured by the pipeline and supplied to subsequent build, testing, reporting, or deployment stages.
Claude Code's configuration system supports explicit permission rules in JSON settings. Anthropic documents that settings.json controls, among other behavior, what Claude Code can execute without asking.
For example, permissions.allow can authorize specific commands while permissions.deny prevents sensitive operations. Project-level .claude/settings.json can be version-controlled so CI behavior is reproducible across runners.
This is substantially safer than C, which disables confirmation behavior globally and expands the blast radius beyond the CI workload. D defeats automation because a human must remain present. A is unnecessary because Claude Code is explicitly configurable for automation scenarios.
The principle is least-privilege non-interactive configuration: authorize only the commands and resources required by the pipeline and capture machine-readable output for downstream processing. Relevant Claude Developer topics are Claude Code automation, configuration management, permissions, CI/CD integration, headless execution, and reproducible project settings.
80. Frage
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?
Antwort: D
Begründung:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.
81. Frage
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