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| Section | Weight | Objectives |
|---|---|---|
| Model Selection and Optimization | 16.8% | - Performance and Cost Optimization
|
| Agents and Workflows | 14.7% | - Subagents and Agentic Frameworks
|
| Tools and MCPs | 10.6% | - Model Context Protocol
|
| Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Applications and Integration | 33.1% | - Claude API and Client SDKs
|
| Eval, Testing, and Debugging | 2.6% | - Testing and Debugging
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
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91. Frage
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?
Antwort: B
Begründung:
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.
92. 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.
93. Frage
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?
Antwort: D
Begründung:
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .
94. Frage
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?
Antwort: A
Begründung:
A is the appropriate configuration-change control. The supplied examination item selects A . A prompt is not merely editorial text; in a Claude application it functions as executable behavioral configuration. Changing a prompt can alter output structure, field population, language, classifications, tool-use decisions, refusal behavior, or other assumptions on which downstream consumers depend.
Staging success therefore proves only the scenarios actually covered by staging. Before production rollout, the team must perform impact analysis across every dependent system, identify contractual expectations, run representative regressions, and coordinate changes where a consumer may be affected. This is especially important where downstream applications parse structured output or expect stable semantics.
Anthropic's Structured Outputs guidance illustrates why interface contracts matter: missing fields, inconsistent types, and schema violations can break consuming applications. Even when output remains syntactically valid, prompt changes can produce semantic changes that require consumer validation.
B assumes staging coverage is universal. C communicates the change without determining its consequences.
D arbitrarily defers systems instead of assessing them.
Relevant Claude Developer topics: Confia Management, configuration impact assessment, prompt versioning, dependency management, change control, downstream contracts, regression testing, and coordinated deployment .
95. Frage
You are building a Claude application that needs to deliver model output to end users as it is generated, instead of waiting for the full response to complete.
The Claude API mechanism you would use is...
Antwort: D
Begründung:
Option B is the intended and technically correct answer. The supplied Claude Developer exam source selects streaming responses. Streaming allows the client application to begin receiving output before generation of the complete message has finished, reducing perceived latency for interactive user experiences.
Anthropic's official Messages API documentation states that setting stream: true causes responses to be delivered incrementally through Server-Sent Events (SSE) . Claude can stream text deltas as well as other event types, including structured tool-use and extended-thinking events where applicable. Anthropic's SDKs provide corresponding synchronous or asynchronous streaming helpers.
The application consumes events as they arrive, updates the UI progressively, and then handles the final event or stop reason when generation completes. This architecture is particularly useful for chat interfaces and other latency-sensitive interactive experiences.
A addresses output structure, not progressive transmission. C is designed for asynchronous, throughput- oriented processing rather than immediate user interaction. D reduces processing cost and latency for repeated prompt prefixes but does not itself turn a response into an incremental stream.
Relevant Claude Developer topics: Messages API, streaming, SSE, incremental tokens, latency, event handling, interactive applications, and API response mechanics .
96. Frage
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