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| Section | Weight | Objectives |
|---|---|---|
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks |
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
| Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls |
| 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 |
| 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 |
| Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria |
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NEW QUESTION # 56
A Claude architect is designing a HIPAA-compliant pipeline that processes patient records.
Which two design decisions directly support HIPAA compliance requirements? (Select two.)
Answer: B,E
Explanation:
Role-based access control directly supports HIPAA's access-control and minimum-necessary principles. The retrieval and orchestration layers must verify the authenticated user's role before protected health information enters the model context. Authorization should be applied to individual records, data sources, tools, and actions, with audit events recording the requesting identity, accessed resource, purpose, and result. Model instructions alone are not a security boundary.
Patient information must also remain within services and processing activities covered by the organization's Business Associate Agreement. Anthropic states that PHI processing requires a HIPAA-ready organization and an accepted BAA, and that only specified eligible services and configurations are covered. Patient data must not be submitted through training, feedback, beta, third-party, or integration pathways that fall outside the applicable agreement. A BAA is not blanket authorization; architects must verify feature eligibility and disable inappropriate data-sharing mechanisms.
Reducing max_tokens, selecting a more capable model, and enabling streaming affect output length, quality, or latency. They do not establish authorization, contractual coverage, minimum-necessary access, or compliant PHI handling.
Study Guide references/topics: Anthropic Business Associate Agreement requirements ; HIPAA-ready services; PHI access control; minimum necessary; eligible-service boundaries; auditability.
NEW QUESTION # 57
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.
Answer:
Explanation:
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.
NEW QUESTION # 58
Engineering leadership wants to roll out Claude Skills to 280 developers across 14 teams. Skills will encode internal coding standards, code-review checklists, and incident-postmortem templates. Leadership has asked how to govern Skill authorship so that Skills remain trustworthy without bottlenecking on a single central team.
Which governance model should you recommend?
Answer: B
Explanation:
Federated authorship allows each domain team to maintain the procedural knowledge it understands while preserving organization-wide trust through a central publication gate. Team authors can update coding standards, review practices, and postmortem procedures without waiting for a single platform group to write every change. Central reviewers can validate ownership, security, tool permissions, duplication, versioning, testing evidence, and compliance before publication. Per-developer or fully decentralized publication creates inconsistent and potentially unsafe Skills. Fully centralized authorship becomes a scalability bottleneck and separates content maintenance from domain expertise. Anthropic describes Skills as packages of instructions, scripts, and resources and warns that repository Skills may grant broad tool access, making formal review essential. Claude Code Skills
NEW QUESTION # 59
You are compiling factors that should drive the choice between Model Context Protocol (MCP), direct API integration, and agent-to-agent handoff.
Which two factors belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: A,E
Explanation:
The interaction model and portability requirement are architectural decision drivers. MCP is appropriate when tools or data sources must be exposed through a standardized interface reusable across compatible AI clients.
Direct API integration is often preferable for a narrow, stateless, latency-sensitive call where the application already owns the integration logic. Agent-to-agent handoff is better suited to stateful or longer-running delegation between independently responsible agents. Documentation quality and team familiarity influence implementation effort but do not determine the correct integration pattern. Encryption in transit is a mandatory security property for production integration rather than a differentiating architectural requirement; each viable approach must provide it. Anthropic distinguishes direct Messages access from stateful managed- agent infrastructure, while MCP supplies a standardized tool integration layer. Claude Platform documentation
NEW QUESTION # 60
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Answer:
Explanation:
Explanation:
Persist the validated output for downstream consumption and audit - Output Handling Order prompt sections so cacheable content appears before per-request content - Prompt Construction Summarize prior conversation history when full history is no longer needed - Input Preparation Validate the model's structured output against the expected schema - Output Handling Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching
NEW QUESTION # 61
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