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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost |
| Topic 2: Integration | 19% | - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations |
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment |
| Topic 4: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
| Topic 5: Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance |
| Topic 6: 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 |
| Topic 7: 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 |
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NEW QUESTION # 42
You are identifying signals that a deployment should re-enter design rather than continue iterating in place.
Which signal most directly indicates the need for a new design cycle?
Answer: A
NEW QUESTION # 43
A security audit uncovers two issues: (1) all end users share a single API key, and (2) tool calls are executed without logging the initiating user.
Which two mitigations directly address these specific findings? (Select two.)
Answer: A,B
Explanation:
Per-user, scope-restricted OAuth tokens directly eliminate the shared-credential weakness. Each token represents a distinct user or delegated identity, carries only the permissions required for that user's role, and can be revoked independently. This supports least privilege, limits the blast radius of credential compromise, and enables authorization decisions to be tied to a specific principal instead of an undifferentiated shared API key.
Actor attribution in tool-call logs directly resolves the second finding. Each audit event should record the authenticated user, delegated service identity, session or request identifier, tool name, authorization decision, relevant resource, timestamp, and outcome. This establishes accountability and permits incident investigators to reconstruct who initiated a consequential action.
Moving credentials into a server-side secret store is sound secret-management practice, but it does not by itself correct the shared-identity problem: a centrally stored key can still represent every user as the same principal. Schema validation protects downstream processing, while retrieval-layer RBAC protects information access; neither addresses the two specific audit findings.
These controls should be reinforced by short-lived tokens, server-side authorization enforcement, sensitive- field redaction, and tamper-resistant audit-log retention.
Study Guide references/topics: [Claude Code security](https://docs.anthropic.com/en/docs/claude-code
/security); [MCP authentication and OAuth] (https://docs.anthropic.com/en/docs/claude-code/mcp); least privilege; identity propagation; auditable tool execution.
NEW QUESTION # 44
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: B,D
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 # 45
You are designing the prompt for a ticket-triage classifier with thirty well-defined categories and clear category descriptions in the prompt.
Which technique is most appropriate as the starting point?
Answer: B
Explanation:
A clearly defined closed-set classification task should begin with a concise zero-shot prompt containing the permitted categories, unambiguous category descriptions, and the required output structure. This provides a simple, inexpensive baseline that can be measured against a representative evaluation set. Chain-of-thought prompting adds unnecessary latency and token usage when the routing decision does not require substantive multi-step reasoning. Permitting Claude to invent categories would violate the closed-set contract and complicate downstream automation. Omitting descriptions would increase ambiguity among potentially overlapping labels. If evaluation later reveals persistent confusion between particular categories, the architect can introduce targeted examples or refine the definitions. Prompt complexity should be added in response to measured failure patterns, not assumed to be necessary before establishing baseline performance. Anthropic:
Prompt engineering overview
NEW QUESTION # 46
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: D
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 # 47
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