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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
| Topic 3: Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation |
| Topic 4: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Topic 5: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques |
| Topic 6: 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 7: Integration | 19% | - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools |
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NEW QUESTION # 50
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 # 51
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: A,D
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 # 52
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
Answer:
Explanation:
Explanation:
Function-level or section-level chunking for code modules - Code or Hierarchical Specifications Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications Per-record chunking where each record is one chunk - Heterogeneous Short Records Semantic chunking along clause or paragraph boundaries - Long Structured Documents Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
NEW QUESTION # 53
You are supporting an EU-based deployment with GDPR obligations.
Which combination of measures best supports the deployment's GDPR posture?
Answer: D
Explanation:
Option A combines the contractual, technical, and operational measures relevant to a defensible GDPR posture. For commercial services, the Data Processing Addendum defines processor obligations and incorporates safeguards such as Standard Contractual Clauses where applicable. Anthropic states that its DPA is incorporated into its Commercial Terms, while the customer remains responsible for determining lawful purposes, issuing processing instructions, and fulfilling controller obligations.
A defined retention configuration ensures personal data is not retained indefinitely without a documented purpose. Redacting personal information that is unnecessary for the task implements data minimization and reduces exposure in prompts, logs, retrieval stores, and model outputs. Documented data-subject-rights procedures support access, correction, deletion, restriction, and other applicable requests by ensuring the organization can locate and act on relevant records.
Enterprise deployment alone does not establish compliance; the organization must configure and govern the service consistently with its processing activities, risk assessment, lawful basis, and contractual commitments.
Options B, C, and D intentionally remove those controls or promote excessive collection.
Study Guide references/topics: [Anthropic Data Processing Addendum guidance](https://privacy.anthropic.
com/en/articles/7996862-how-do-i-view-and-sign-your-data-processing-addendum-dpa); [commercial data- retention practices] (https://privacy.anthropic.com/en/articles/7996866-how-long-do-you-store-my- organization-s-data); GDPR data minimization; retention governance; data-subject-rights procedures.
NEW QUESTION # 54
You are compiling continuity practices that span the deployment lifecycle.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: C,E
NEW QUESTION # 55
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