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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies |
| Topic 2: Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs |
| Topic 3: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Topic 4: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost |
| Topic 5: Claude Models, Prompting & Context Engineering | 13% | - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails |
| Topic 6: 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 7: Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability |
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NEW QUESTION # 42
A technical team is cataloging risks specific to Claude's use in a document-grounded Q & A system.
Which two items represent failure modes that are intrinsic to LLM-based systems rather than generic software defects? (Select two.)
Answer: B,E
Explanation:
Options A and B describe model-specific behavioral risks. An overly broad safety response is a false positive caused by how the language model interprets contextual or surface-level signals. A plausible but unsupported answer is a hallucination: the generated statement appears credible but lacks grounding in the retrieved evidence. Anthropic recommends grounding factual responses in source material, requiring citations, allowing Claude to express uncertainty, and verifying generated claims. Options C, D, and E are conventional infrastructure or application failures involving certificates, routing, and database connectivity. They can occur in systems that do not use an LLM and should be addressed through standard monitoring, configuration management, retry policies, and operational testing. Anthropic: Reduce hallucinations
NEW QUESTION # 43
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
Answer: A
Explanation:
Option C addresses both components responsible for the excessive cost. Anthropic prompt caching allows stable, repeatedly submitted prompt material-such as system instructions, tool definitions, and reusable background information-to be placed in a consistent prefix. After that prefix is written to the cache, qualifying subsequent requests can reuse it at the lower cache-read cost instead of repeatedly processing the same content at the standard input-token rate.
Retrieval must be optimized separately. Supplying an entire document collection or excessively deep search results increases cost, consumes context capacity, and may reduce answer quality by surrounding the relevant evidence with distracting material. Retrieval should select the smallest set of authoritative passages that provides sufficient evidence for the current query. This typically requires relevance scoring, deduplication, metadata filtering, reranking, and explicit token-budget limits.
Increasing retrieval depth or expanding the repeated system prompt directly worsens the identified cost driver.
Moving every request to a heavier model changes the unit economics but does not correct inefficient context construction. The recommended optimization therefore combines prefix caching with query-specific context pruning, followed by evaluation to confirm that reduced context does not lower task accuracy.
Study Guide references/topics: [Prompt caching](https://docs.anthropic.com/en/docs/build-with-claude
/prompt-caching); [effective context engineering] (https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents); retrieval precision; token-cost analysis; cache-prefix stability.
NEW QUESTION # 44
You are building an ethics review checklist for deployments supported by artificial intelligence (AI). Which two checks belong on the list? (Select two.) Each correct answer presents a complete solution.
Answer: B,D
Explanation:
High-impact AI-supported decisions must retain clearly assigned human accountability. Claude may provide recommendations, classifications, or generated content, but responsibility for consequential outcomes cannot be transferred to the model. An ethics review should also investigate whether outputs make unsupported generalizations about individuals or groups, because such behavior can produce discriminatory, misleading, or harmful outcomes. Therefore, B and E directly address ethical accountability and fairness. Licensing in option A is an important legal and procurement concern, but it is not the strongest ethics-focused control presented.
Latency and throughput in option D are operational performance requirements. Option C is inappropriate because ethics risks are not limited to outputs above a confidence threshold; confidence values may also be poorly calibrated and do not prove that an output is fair, accurate, or ethically acceptable.
NEW QUESTION # 45
A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)
Answer: A,C
Explanation:
Expectation alignment begins with measured evidence. Option A gives stakeholders the median experience through p50 latency and the slower-tail experience through p95, then compares both with the agreed SLA.
The measurements should reflect representative input sizes, concurrency, retrieval activity, tool calls, geographic routing, and cache conditions.
If the target remains infeasible after reasonable optimization, Option E establishes the correct governance response. The architect should collaboratively revise the SLA, scope, cost envelope, model choice, or user- experience design rather than silently accepting a breach or making unilateral changes. Any revision should document the business impact and accepted trade-offs.
Option B commits all resources before the problem's severity, causes, and business priority have been established. Option C wrongly treats latency as uncontrollable; model selection, prompt length, retrieval depth, caching, streaming, and architecture can materially affect it. Option D introduces an unvalidated provider and could create quality, security, compliance, and integration regressions.
Expectation management is not merely communicating bad news. It requires transparent measurements, credible optimization options, explicit trade-offs, and jointly approved commitments that the production architecture can actually satisfy.
Study Guide references/topics: SLA alignment; p50 and p95 latency; stakeholder negotiation; evidence-based communication; architectural trade-offs; production constraints.
NEW QUESTION # 46
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: B,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 # 47
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