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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Stakeholder management - Solution lifecycle management - Discovery and requirements gathering - Architecture documentation - Service-level agreements |
| Topic 2: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - System prompts and prompt templates - Guardrails - Context window optimization |
| Topic 3: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Developer enablement - Claude tooling configuration for teams - AI-assisted developer workflows |
| Topic 4: Solution Design & Architecture | 17% | - End-to-end architecture design - Architectural patterns
- Translating business problems into Claude-based AI solutions - Decomposition techniques for complex problem solving - Multi-agent systems and orchestration |
| Topic 5: Integration | 19% | - Authentication and authorization analysis - RAG pipeline design
|
| Topic 6: Evaluation, Testing & Optimization | 16% | - A/B testing - Production monitoring and optimization - Evaluation framework design - Evaluation metrics and datasets - System issue diagnosis - Cost and performance optimization |
| Topic 7: Governance, Safety & Risk Management | 14% | - Security and risk management - AI safety and guardrails - Human-in-the-loop validation - Regulatory and compliance requirements - Ethical AI considerations |
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126. Frage
A business sponsor has requested an AI solution to "improve customer experience." The sponsor cannot articulate which customer journey is failing, which metric reflects the failure, or which decisions the AI should support. The sponsor is asking you to begin design work next week.
Which delegation-competency action should you take first?
Antwort: A
Begründung:
The request is not yet design-ready because it lacks a defined failure, affected user journey, supported decision, target population, and measurable outcome. The architect should therefore facilitate structured discovery rather than prematurely selecting an AI pattern or model.
Discovery should identify where the current customer journey breaks down, who experiences the problem, what decision or task requires assistance, what data is available, and which metric would demonstrate improvement. The team can then establish baseline performance, target thresholds, operational constraints, risks, and explicit non-goals. Anthropic's evaluation guidance states that an LLM application should begin with clearly defined success criteria and a method for measuring them. Define Success Criteria and Build Evaluations Option A abandons the sponsor instead of helping translate a business concern into an actionable problem.
Option B risks producing a generic demonstration unrelated to a validated customer need. Option C substitutes the architect's assumptions for stakeholder evidence and may commit the organization to the wrong scope.
Competent delegation begins by defining the decision, intended outcome, authority boundaries, and target metric before determining which responsibilities should be assigned to Claude, conventional software, or human reviewers.
Study Guide references/topics: Structured discovery; delegation competency; problem definition; target metrics; decision support; measurable business outcomes.
127. Frage
You are evaluating model-selection claims used by a peer team.
For each statement, select yes if the statement is generally true. Otherwise, select no.
Antwort:
Begründung:
Explanation:
Yes, Yes, No, Yes, No
More capable reasoning configurations generally require greater computation, produce higher latency, and may consume larger token or rate-limit allocations than lighter alternatives. This makes the first statement generally accurate, although actual performance must be measured under the organization's specific provider and service tier.
Representative workload testing is essential because generic benchmark rankings do not establish performance on the organization's prompts, data, edge cases, tools, or required output format. Anthropic recommends benchmark tests specific to the use case, using actual prompts and data, followed by comparison of accuracy, quality, edge-case behavior, and cost. Choosing the Right Model The strongest model is not automatically the correct production choice. Model selection must balance capability, speed, cost, throughput, and operational constraints. Tiered routing implements this principle by serving predictable requests with an efficient model and escalating difficult cases to a more capable model.
Anthropic documents multi-model strategies in which lower-cost workers handle most traffic while harder decisions are escalated.
Recency is also not a valid selection criterion. A newly announced model must pass the workload's existing evaluation, security, latency, and cost gates before replacing a validated production model.
Study Guide references/topics: Model trade-offs; representative evaluations; tiered routing; escalation architectures; cost-capability optimization; model migration validation.
128. Frage
You are comparing patterns for a batch document-classification job that follows fixed steps: extract metadata, classify, summarize, and persist.
Which pattern is the best fit and why?
Antwort: D
Begründung:
A workflow is appropriate when processing stages and transitions are known in advance. Metadata extraction, classification, summarization, and persistence can be represented as deterministic nodes with explicit schemas, validation rules, retry policies, and failure handling. This provides predictable execution, cost, observability, and testability without paying for repeated model-driven planning. Anthropic's Building Effective AI Agents distinguishes workflows, where predefined code paths control execution, from agents, where the model dynamically determines its process. Option A reaches the correct pattern for an incorrect reason: workflows and agents can both invoke tools. Options B and C introduce unnecessary autonomy and make unsupported claims about consistency or universal accuracy.
Study Guide references/topics: Workflow versus agentic patterns; deterministic orchestration; batch processing; structured outputs; predictable cost; error handling.
129. Frage
A team manager wants all engineers working on the same repository to share identical MCP server definitions without manual synchronization.
Which configuration approach satisfies this requirement?
Antwort: A
Begründung:
Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository's normal clone, pull, review, and versioning workflow.
Anthropic's scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.
Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.
Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs
/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.
130. Frage
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.
Antwort: A,C
131. Frage
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