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| Section | Weight | Objectives |
|---|---|---|
| Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs |
| Integration | 19% | - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools - Design authentication, authorization, and observability |
| Solution Design & Architecture | 17% | - Align solutions to business value pillars - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs |
| Developer Productivity & Operational Enablement | 7% | - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance |
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100. Frage
You are an architect supporting the iteration phase of a deployed Claude-based system.
Which activity most directly fits this phase?
Antwort: A
Begründung:
Iteration is an evidence-driven lifecycle phase. Option D combines three essential inputs: production telemetry, sampled output evaluation, and stakeholder feedback. Telemetry exposes latency, cost, errors, tool failures, retrieval behavior, and operational anomalies. Output sampling identifies quality, safety, grounding, or consistency failures that infrastructure metrics may not reveal. Stakeholder feedback establishes whether the system continues to satisfy real business and user needs.
The architect should prioritize the highest-impact validated problem, define the intended improvement, and test the proposed change against the existing evaluation framework and reference set. Anthropic emphasizes that evaluations make behavioral changes visible before they affect users and that their value compounds throughout an agent's lifecycle. Demystifying Evals for AI Agents Option A replaces targeted iteration with unnecessary redesign. Option B incorrectly treats deployment as the end of measurement. Option C removes the baseline needed to distinguish genuine improvement from regression or noise.
A mature iteration cycle preserves evaluation continuity, adds production failures as new test cases, validates changes offline, conducts controlled rollout, monitors results, and updates documentation and known limitations.
Study Guide references/topics: Production iteration; telemetry; sampled evaluation; stakeholder feedback; regression testing; evaluation continuity; change prioritization.
101. Frage
You are designing a human-in-the-loop validation workflow for a new Claude-based deployment and must complete the design steps before piloting the workflow.
Which two steps must be completed BEFORE piloting the workflow with a representative subset of traffic?
(Select two.)
Each correct answer presents part of the solution.
Antwort: A,E
Begründung:
The workflow must first identify where human judgment is necessary. Option E applies risk-based oversight by examining each decision's impact and reversibility. High-impact, irreversible, externally binding, privacy- sensitive, or legally consequential actions require stronger review than low-risk draft generation or easily reversible recommendations.
After locating those control points, Option A defines how work reaches reviewers. The sampling strategy should specify whether review is universal, random, risk-triggered, confidence-based, or stratified. Escalation criteria should cover policy violations, uncertain outputs, conflicting evidence, sensitive data, failed validation, and cases exceeding the reviewer's authority.
Anthropic recommends human confirmation for decisions with meaningful real-world consequences and actions requiring affirmative consent. Computer Use Security Considerations Option B necessarily follows the pilot because findings must exist before design iteration. Reviewer onboarding and operational documentation in Options C and D are important readiness activities, but they follow the core design decisions being tested by this item: where oversight is required and how cases are sampled and escalated.
The completed pilot plan should subsequently include trained reviewers, documented procedures, queue capacity, SLAs, audit evidence, and measured inter-reviewer agreement.
Study Guide references/topics: Human-in-the-loop design; impact and reversibility; oversight points; sampling; escalation criteria; consequential decisions.
102. Frage
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Antwort: A
Begründung:
Regression testing determines whether a change has broken behavior that previously satisfied the defined acceptance criteria. A stable reference set preserves known-good cases and enables the updated prompt to be compared with the prior baseline using consistent scoring rules. This makes Option B directly aligned with the described failure. Adversarial testing focuses on malicious or boundary inputs, such as prompt injection.
Smoke testing confirms that the system remains broadly available, but it does not measure whether previously correct model behavior has degraded. Integration testing verifies contracts and interactions among components and may not detect output-quality regressions caused specifically by a prompt change. Anthropic recommends task-specific evaluations that mirror real workloads, include edge cases, and measure performance against explicit success criteria throughout prompt iteration. Anthropic: Define success criteria and build evaluations
103. Frage
You are supporting a team whose Claude Code sessions consistently load 60 or more MCP tools from many servers, exhausting context budget before the session begins.
Which adjustment most directly addresses this issue without removing capability?
Antwort: B
Begründung:
Tool Search applies progressive discovery to a large tool catalog. Instead of inserting every MCP tool's complete JSON schema and description into the initial context, Claude receives limited discovery information and loads the full definitions only when a task requires them. This preserves the available context for instructions, repository content, conversation history, and tool results without removing the underlying capabilities.
Anthropic's Claude Code documentation states that Tool Search keeps MCP context usage low by deferring tool definitions until Claude needs them. Only the tools actually selected enter the working context. Claude Code MCP Tool Search Anthropic's advanced-tool-use guidance also describes conventional multi-server configurations consuming tens of thousands of tokens before work begins, while on-demand discovery substantially reduces that overhead. Advanced Tool Use Option B solves context pressure by destroying required functionality. Option C worsens the constraint by adding more prompt tokens. Option D expands the exact source of context bloat.
The team should also improve server and tool descriptions so discovery returns the correct tools, monitor tool- selection quality, and keep frequently required foundational tools non-deferred only when evaluations justify the additional context cost.
Study Guide references/topics: MCP scaling; Tool Search; progressive discovery; deferred schemas; context- budget management; tool-selection accuracy.
104. Frage
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Antwort: A
Begründung:
Regression tests determine whether behavior that previously met requirements still passes after a change. A stable reference set provides identical inputs, expected outcomes, and scoring rules across prompt versions, allowing the team to attribute failures to the update. Integration tests verify interactions among components but may not measure detailed output behavior. Adversarial tests target attacks and misuse cases, while smoke tests establish only that essential system paths remain available. The failed cases should be added to or retained within the regression suite, and the old and new prompt should be compared under the same model, parameters, tools, and retrieval context. Anthropic describes regression evaluations as protection against behavioral backsliding. Demystifying evals for AI agents
105. Frage
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