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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Topic 2: Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance |
| Topic 3: Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars |
| Topic 4: Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost |
| Topic 6: Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs |
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NEW QUESTION # 115
You are choosing the level of detail for an implementation guide. The audience is a delivery team that will build the deployment.
Which guidance composition best serves them?
Answer: C
Explanation:
A delivery team needs implementation-level direction rather than an executive architecture summary.
Component responsibilities establish ownership and boundaries, while interface contracts define schemas, accepted inputs, outputs, errors, and behavioral expectations. Sequence diagrams show how components interact across dominant and failure flows. Configuration tables with defaults prevent inconsistent assumptions between environments. Operational runbooks ensure that deployment, recovery, monitoring, and routine maintenance are addressed during implementation rather than improvised after release. Options B, C, and D omit one or more elements required to translate architectural intent into a buildable and supportable system. The guide should enable normal delivery work without continuous dependence on undocumented knowledge held by the architect.
Study Guide references/topics: Implementation documentation; component contracts; sequence diagrams; configuration management; operational readiness.
NEW QUESTION # 116
An architect at Trenova Systems, Inc. is reviewing two draft communication packages for the same architectural decision. Package 1 contains a threat model, residual-risk register, and control-mapping table.
Package 2 contains a list of capabilities delivered and feature-level roadmap dependencies.
Which two audiences are correctly matched to these packages? (Select two.)
Answer: A,C
Explanation:
Package 1 is designed for security and compliance review because its threat model identifies relevant threats and attack paths, its residual-risk register documents the risks remaining after controls, and its control- mapping table connects safeguards to organizational or regulatory requirements. Package 2 is designed for the product manager because delivered capabilities and roadmap dependencies directly support prioritization, release planning, sequencing, and lifecycle ownership. An engineering implementation team would ordinarily need component diagrams, interface contracts, configuration schemas, deployment instructions, and operational runbooks rather than primarily risk-governance artifacts. An executive sponsor generally needs a concise outcome, cost, risk, and decision summary instead of feature-level dependency details. Effective architecture communication must therefore adapt the content and level of detail to each audience's responsibilities and decision rights.
NEW QUESTION # 117
You are reviewing instrumentation in a multi-agent system.
Which two findings constitute valid observability gaps in the instrumentation? (Select two.) Each correct answer presents a complete solution.
Answer: B,E
Explanation:
Model identity and version are essential diagnostic dimensions. Without them, operators cannot correlate a behavioral change with a model migration, reproduce the conditions of a failed turn, compare performance among model versions, or distinguish model drift from prompt, retrieval, and application defects. Therefore, the omission described in Option D is a genuine observability gap.
Option E is equally significant. A multi-agent execution normally crosses orchestration, model, agent, MCP, and tool boundaries. If the request-scoped correlation identifier or distributed trace context is lost at any boundary, the resulting spans cannot reliably be reconstructed into one end-to-end transaction. Anthropic's monitoring guidance explains that distributed tracing links a user prompt to the API requests and tool executions it initiates; it also documents propagation of W3C trace context to supported subprocesses and outbound MCP requests. Claude Code Monitoring Options A and B describe healthy telemetry coverage rather than gaps. Option C is also appropriate because recording tool interactions supports investigation, while redaction reduces the risk of exposing sensitive data in logs.
Study Guide references/topics: Integration-observability challenges at scale; distributed tracing; correlation identifiers; model-version attribution; privacy-aware tool telemetry.
NEW QUESTION # 118
You are an architect supporting the iteration phase of a deployed Claude-based system.
Which activity most directly fits this phase?
Answer: A
Explanation:
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.
NEW QUESTION # 119
You are defining an SLA for a Claude-based assistant.
Which SLA definition is most operationally meaningful?
Answer: C
Explanation:
An operational SLA must be objectively measurable and enforceable. Option B defines the metric, percentile, threshold, and observation window, allowing both parties to determine whether service performance complied with the agreement. A complete SLA also defines what follows a breach, such as escalation, remediation, service credits, capacity changes, or architectural review. Percentile latency is preferable to an average because it exposes degraded tail performance experienced by a meaningful portion of users. Anthropic provides usage, cost, and operational data that can support monitoring and alerting through the Usage and Cost API . Options A and C are subjective. Option D remains incomplete because neither assessment periods nor breach handling can be applied consistently.
Study Guide references/topics: SLA construction; p95 latency; thresholds; evaluation windows; breach consequences; operational measurement.
NEW QUESTION # 120
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