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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams - Debugging and operational issue resolution |
| Topic 2: Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Stakeholder management - Architecture documentation - Solution lifecycle management - Service-level agreements - Communicating architectural decisions |
| Topic 3: Solution Design & Architecture | 17% | - Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - Architectural patterns
- Decomposition techniques for complex problem solving - Alignment with business value, cost, performance, and SLAs |
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Context window optimization - Guardrails - Claude model selection and trade-offs - System prompts and prompt templates |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Evaluation framework design - Cost and performance optimization - Evaluation metrics and datasets - System issue diagnosis - A/B testing - Production monitoring and optimization |
| Topic 6: Integration | 19% | - Enterprise system integration - Authentication and authorization analysis - Claude integration mechanisms
|
| Topic 7: Governance, Safety & Risk Management | 14% | - Security and risk management - AI safety and guardrails - Human-in-the-loop validation - Ethical AI considerations - Regulatory and compliance requirements |
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NEW QUESTION # 21
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,E
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 # 22
You are building an ethics-review checklist for deployments supported by artificial intelligence.
Which two checks belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: C,E
Explanation:
An ethics review must examine unsupported generalizations, stereotyping, disparate treatment, and other conclusions about people that are not justified by the evidence. It must also preserve identifiable human accountability for consequential decisions; responsibility cannot be transferred to a probabilistic system.
Licensing is a legal and commercial-control question, while latency and throughput are operational requirements. Both remain important, but neither is the central ethical test presented here. Restricting review to low-confidence or threshold-triggering outputs is also inadequate because models can express harmful or biased conclusions with high confidence. Ethical review should cover representative populations, protected and intersectional groups, high-impact outcomes, override mechanisms, transparency, and channels for challenge or appeal.
Study Guide references/topics: Bias and unsupported generalization; human accountability; consequential decisions; ethical oversight.
NEW QUESTION # 23
You are explaining the precedence of Claude Code configuration scopes to the team.
Which precedence ordering, from highest to lowest, is correct?
Answer: C
Explanation:
The correct general precedence is managed configuration, command-line arguments, local configuration, project configuration, and then user configuration.
Managed configuration has the highest authority because it contains centrally administered organizational and compliance controls that ordinary engineers must not be able to override. Command-line arguments provide temporary session-level overrides where the relevant setting permits them. Local settings apply to one user within a specific repository and take precedence over the shared project configuration. Project settings apply to every contributor using that repository and override general user preferences. User settings form the lowest tier because they provide personal defaults across projects and apply only when no higher-precedence source supplies a value.
Anthropic documents this order explicitly: managed, command line, local, project, and user. It also notes that permission rules may merge rather than behave as simple scalar overrides, and certain security-sensitive keys have specialized precedence behavior. Claude Code Configuration Scopes The ordering allows personal customization without permitting it to defeat repository conventions or centrally mandated security requirements. Architects must still examine the merge behavior of the individual configuration property rather than assuming every array or permission rule is replaced wholesale.
Study Guide references/topics: Claude Code configuration; scope precedence; managed policies; local overrides; project settings; user defaults.
NEW QUESTION # 24
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 # 25
You are selecting a model for a new production workload and must complete the upstream steps before testing candidate models empirically.
Which two steps must be completed BEFORE running a representative sample on a candidate model? (Select two.) Each correct answer presents part of the solution.
Answer: A,B
NEW QUESTION # 26
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