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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Multi-agent systems and orchestration - Alignment with business value, cost, performance, and SLAs - Translating business problems into Claude-based AI solutions - Architectural patterns
- Decomposition techniques for complex problem solving |
| Topic 2: Integration | 19% | - RAG pipeline design
- Claude integration mechanisms
|
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Architecture documentation - Discovery and requirements gathering - Solution lifecycle management - Stakeholder management - Communicating architectural decisions |
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Prompt reuse and context engineering strategies - Context window optimization - Claude model selection and trade-offs - Guardrails |
| Topic 5: Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Cost and performance optimization - A/B testing - Production monitoring and optimization - Evaluation framework design - Evaluation metrics and datasets |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Debugging and operational issue resolution - Developer enablement - Claude tooling configuration for teams |
| Topic 7: Governance, Safety & Risk Management | 14% | - Regulatory and compliance requirements - AI safety and guardrails - Security and risk management - Ethical AI considerations - Human-in-the-loop validation |
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NEW QUESTION # 73
A Claude architect is implementing safety controls for a customer-facing advice assistant that must never provide regulated investment recommendations.
Which two guardrail implementations most directly enforce this constraint? (Select two.)
Answer: C,D
Explanation:
The required control is best implemented through layered prevention. The system prompt should explicitly classify personalized investment recommendations as out of scope, instruct Claude not to recommend particular securities or transactions, and provide standardized refusal language. Fixed refusal behavior reduces ambiguity and gives the model a clear alternative response when users request prohibited advice or attempt to reframe it indirectly.
An output classifier supplies an independent enforcement layer after generation but before delivery. It should detect prohibited recommendation language, individualized buy-or-sell instructions, portfolio allocations, and equivalent regulated content. A detected violation must block or replace the response rather than merely record it. This protects against prompt circumvention, unexpected phrasing, and occasional model noncompliance.
Increasing temperature makes responses less predictable and weakens control reliability. Session-length limits regulate volume, not content. SIEM logging is valuable for monitoring, investigation, and compliance evidence, but it is detective rather than preventative: the prohibited recommendation may already have reached the customer.
The completed design should evaluate both layers using adversarial prompts, paraphrases, multi-turn escalation, and classifier false-negative testing.
Study Guide references/topics: [Mitigating jailbreaks and prompt injections](https://docs.anthropic.com/en
/docs/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks); layered guardrails; explicit scope boundaries; standardized refusals; output classification; pre-delivery enforcement.
NEW QUESTION # 74
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 # 75
The compliance team has authored a regulatory disclosure procedure that must be applied identically across customer service, sales, and onboarding workflows. The procedure changes when regulators issue updates, currently four to six times per year. You are designing how the procedure will be packaged for use by Claude across all three workflows.
Which two design decisions should you include? (Select two.)
Each correct answer presents part of the solution.
Answer: A,E
Explanation:
The procedure should have one authoritative implementation and one accountable owner. Packaging it as a compliance-owned Skill allows the instructions, examples, templates, and prohibited patterns to be maintained together. Referencing that same Skill from all three workflows ensures that updates propagate consistently without requiring separate prompt changes. Embedding copies in multiple system prompts or allowing workflow teams to rewrite the procedure creates configuration drift and inconsistent regulatory behavior. A retrieval corpus is appropriate for searchable reference material, but the requirement concerns a fixed operational procedure that Claude must follow, making a Skill the stronger abstraction. The Skill should be versioned, reviewed, tested, and released through a controlled publication process. Claude Code Skills
NEW QUESTION # 76
You are presenting an architectural decision to a mixed audience that includes an executive sponsor and the engineering leads who will implement the decision.
Which presentation strategy best serves both audiences?
Answer: A
NEW QUESTION # 77
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 # 78
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