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| Section | Objectives |
|---|---|
| Topic 1: Solution Design | - Claude platform and solution architecture - Model, architecture, context, and deployment strategy selection |
| Topic 2: Governance, Safety, and Risk | - Safety controls and fail-closed system design - Security, compliance, risk management, and governance |
| Topic 3: Stakeholder Engagement and Solution Lifecycle | - Solution lifecycle, handoff, adoption, and operational enablement - Stakeholder discovery and architectural trade-off communication |
| Topic 4: Evaluation and Optimization | - Model and architecture optimization - Evaluation design and acceptance criteria |
| Topic 5: Enterprise Integration and Production | - Enterprise integration architecture and production readiness - Cost, latency, reliability, and operational design |
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104. Frage
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.)
Antwort: A,E
Begründung:
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.
105. Frage
You are listing characteristics of robust guardrail design for an enterprise deployment.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.
Antwort: A,D
106. Frage
You are listing characteristics of robust guardrail design for an enterprise deployment.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.
Antwort: A,D
Begründung:
Robust guardrails require both preventive enforcement and continuous empirical validation. Per-role tool allow-lists satisfy the preventive requirement by restricting each authenticated role to explicitly authorized tools. Enforcement must occur in the orchestration or permission layer before execution; relying on the model to decide whether a tool call is authorized is not an adequate security boundary. Anthropic recommends allow- list-based validation and provides permission rules and pre-tool-use controls capable of denying calls before they execute.
Adversarial evaluation with regression tracking supplies the validation component. The evaluation set should include prompt injections, indirect injections, encoded instructions, multi-turn escalation, role-confusion attempts, and requests designed to trigger unauthorized tool use. Guardrail performance must then be measured across releases so that changes to prompts, models, tools, or orchestration logic do not silently reduce protection.
Centralized logging and user feedback are useful detective and lifecycle controls, but neither prevents an unsafe action nor proves guardrail effectiveness. Periodically refreshing refusal wording is also insufficient unless the revised behavior is evaluated against defined safety criteria. The strongest design therefore combines deterministic authorization with adversarial regression testing.
Study Guide references/topics: Claude Code permissions and pre-tool enforcement ; tool allow-list guidance ; adversarial evaluations; regression testing; least-privilege orchestration.
107. Frage
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.
Antwort: B,D
Begründung:
Empirical model testing requires a defined decision frame. Option D must occur first because the quality threshold, acceptable latency, expected request volume, and associated cost envelope determine what constitutes a successful result. Without these criteria, a representative-sample test can produce measurements but cannot support a defensible selection.
Option C is the next upstream step. Known model capabilities and the workload profile are used to identify a plausible candidate rather than testing models arbitrarily. Anthropic's model-selection guidance recommends evaluating capability, speed, cost, and effort requirements before narrowing the candidate set. Choosing the Right Model Option A occurs after empirical testing because the architect cannot know which lightest model consistently meets the quality bar until sample results have been evaluated. Option B belongs to ongoing lifecycle management after a new version becomes available. Option E is a deployment-governance activity that follows successful model selection, security review, and readiness validation.
The correct sequence is: define workload criteria, shortlist candidate models, run a representative evaluation, compare quality and operational metrics, select the least costly model that reliably satisfies all gates, and periodically retest the decision.
Study Guide references/topics: Requirements-driven model selection; representative sampling; quality thresholds; latency and volume requirements; capability-cost trade-offs; model lifecycle reassessment.
108. Frage
A loan pre-qualification assistant shows 94 percent approval recommendations that match the human underwriter decision. The fairness team has reviewed approval rate parity across protected groups and reported no significant difference. A board member has asked whether this evidence is sufficient to declare the assistant fair.
Which two Discernment-competency findings should you report? (Select two.) Each correct answer presents part of the solution.
Antwort: C,D
Begründung:
Approval-rate parity measures whether groups receive positive recommendations at similar rates. It does not establish whether false approvals, false denials, sensitivity, specificity, or calibration are comparable across those groups. A system can therefore satisfy aggregate approval parity while imposing materially different error burdens on protected populations. The evaluation must include subgroup confusion matrices, false- positive and false-negative rates, calibration, intersectional analysis, and confidence intervals.
The 94 percent agreement rate measures fidelity to human underwriter decisions, not fairness. Human decisions are not automatically unbiased ground truth. If historical underwriting practices contain structural, procedural, or measurement bias, a model that reproduces those decisions accurately can reproduce the same bias. The reference labels must therefore be independently assessed for legitimacy, consistency, and potential discriminatory effects.
Nothing in the scenario establishes that protected groups were omitted, so D is unsupported. Similarly, sample size may require examination, but the scenario provides no statistical information proving that sample size is the principal deficiency. The evidence already contains two identifiable conceptual gaps regardless of sample size.
Study Guide references/topics: [Defining multidimensional evaluation criteria](https://docs.anthropic.com/en
/docs/build-with-claude/develop-tests); fairness measurement; subgroup error analysis; label and benchmark bias; human-baseline limitations; governance evidence.
109. Frage
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