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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost |
| 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 |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions |
| 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 |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance |
| 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 |
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NEW QUESTION # 124
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 # 125
You are evaluating a Claude-based deployment for adherence to a specific regulation.
Which two steps must be completed BEFORE mapping deployment data flows to specific regulatory clauses?
(Select two.)
Each correct answer presents part of the solution.
Answer: A,B
Explanation:
Regulatory assessment must begin by establishing scope. Option B determines whether the regulation applies by examining processed data, affected jurisdictions, users, and deployment purpose. Without this analysis, clause mapping could apply irrelevant obligations or omit applicable ones. Option E establishes the platform's existing compliance capabilities, including audit records, retention controls, access governance, data residency, and compliance-export functionality. Anthropic's Trust Center provides control and certification information, while the Compliance API supports programmatic access to activity and governance records.
Options A and D occur after regulatory clauses have been mapped and gaps identified. Option C follows prioritization and approval of remediation actions.
Study Guide references/topics: Regulatory applicability; compliance affordance inventory; control mapping; gap assessment; residual risk; remediation sequencing.
NEW QUESTION # 126
You are selecting the documentation set that should accompany a Claude-based deployment at handoff.
Which set is most complete?
Answer: D
Explanation:
A complete handoff package must help different stakeholders understand, operate, troubleshoot, and evolve the Claude-based solution. An architecture overview explains the system's structure and responsibilities, while Architecture Decision Records document important decisions, alternatives, and trade-offs. Component- level diagrams provide implementation context without forcing maintainers to reconstruct dependencies from source code. Operational runbooks and an on-call playbook enable reliable incident detection, diagnosis, escalation, and recovery. Documenting known limitations is particularly important for Claude applications because model behavior can vary with prompts, context, input distribution, and external dependencies.
Options B, C, and D each provide only a partial or temporary understanding of the system. They would create knowledge gaps, increase operational risk, and make future maintenance dependent on individuals rather than durable organizational documentation.
NEW QUESTION # 127
You are a platform architect designing an internal Claude-based assistant that serves both finance analysts and external auditors. Each population must access only documents permitted by its role.
Where should role-based access control be enforced in the pipeline?
Answer: B
Explanation:
Authorization must be enforced before restricted information enters model context. The retrieval service should authenticate the user, resolve role and document permissions, and filter candidate records before prompt construction. This prevents unauthorized content from appearing in prompts, model traces, cached context, tool results, or generated responses. A system-prompt instruction is behavioral guidance, not an enforceable access-control boundary. Post-generation filtering is also inadequate because the model has already received and processed the restricted data, creating exposure through output, logging, or indirect inference. Relying on general refusal behavior provides no deterministic authorization. The correct architecture treats RBAC as an application and data-layer control, with the model receiving only information the requesting principal is entitled to access.
Study Guide references/topics: Retrieval-layer RBAC; authorization before inference; least privilege; sensitive-context isolation.
NEW QUESTION # 128
You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Answer:
Explanation:
Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
NEW QUESTION # 129
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