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The workflow must first identify where human judgment is necessary. Option E applies risk-based oversight by examining each decision’s impact and reversibility. High-impact, irreversible, externally binding, privacy-sensitive, or legally consequential actions require stronger review than low-risk draft generation or easily reversible recommendations.

After locating those control points, Option A defines how work reaches reviewers. The sampling strategy should specify whether review is universal, random, risk-triggered, confidence-based, or stratified. Escalation criteria should cover policy violations, uncertain outputs, conflicting evidence, sensitive data, failed validation, and cases exceeding the reviewer’s authority.

Anthropic recommends human confirmation for decisions with meaningful real-world consequences and actions requiring affirmative consent. Computer Use Security Considerations

Option B necessarily follows the pilot because findings must exist before design iteration. Reviewer onboarding and operational documentation in Options C and D are important readiness activities, but they follow the core design decisions being tested by this item: where oversight is required and how cases are sampled and escalated.

The completed pilot plan should subsequently include trained reviewers, documented procedures, queue capacity, SLAs, audit evidence, and measured inter-reviewer agreement.

Study Guide references/topics: Human-in-the-loop design; impact and reversibility; oversight points; sampling; escalation criteria; consequential decisions.

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The scenario describes indirect prompt injection: malicious instructions are embedded in third-party content that Claude processes as retrieved evidence. Option A addresses the threat at the appropriate boundary by classifying retrieved material as untrusted, screening it before use, and validating the model’s output before it can influence downstream actions.

Anthropic recommends placing untrusted third-party content in clearly identified tool-result structures, explicitly instructing Claude that retrieved content cannot override system or user instructions, screening tool output for injection attempts, and applying output validation. Mitigate Jailbreaks and Prompt Injections

Citations in Option B improve factual traceability but do not prevent embedded instructions from attempting to redirect model behavior. An outbound destination allow-list in Option C limits the damage that a compromised agent can cause through tool calls, but it does not directly detect or neutralize the injected content. Option D is valuable for detecting behavioral regression after a model change, not for controlling a specific malicious document during runtime.

A production implementation should combine content provenance, structural separation, injection screening, least-privilege tools, output-schema validation, and human confirmation for consequential actions.

Study Guide references/topics: Indirect prompt injection; untrusted retrieved content; input classification; output validation; layered runtime guardrails.

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A delivery team needs implementation-level direction rather than an executive architecture summary. Component responsibilities establish ownership and boundaries, while interface contracts define schemas, accepted inputs, outputs, errors, and behavioral expectations. Sequence diagrams show how components interact across dominant and failure flows. Configuration tables with defaults prevent inconsistent assumptions between environments. Operational runbooks ensure that deployment, recovery, monitoring, and routine maintenance are addressed during implementation rather than improvised after release. Options B, C, and D omit one or more elements required to translate architectural intent into a buildable and supportable system. The guide should enable normal delivery work without continuous dependence on undocumented knowledge held by the architect.

Study Guide references/topics: Implementation documentation; component contracts; sequence diagrams; configuration management; operational readiness.

The required sequence must reduce repeated computation without changing the information or model capability used to generate projections. Prompt caching the static 6,000-token policy preamble directly addresses the largest cost contributor while preserving the complete instruction set. Anthropic states that cache reads cost substantially less than uncached input tokens, making repeated, stable prompt prefixes an ideal caching target. Prompt Caching

Caching frequently reused historical-sales chunks applies the same principle to the retrieval layer. When identical, version-controlled chunks are repeatedly fetched and supplied to the model, caching their retrieval or reusable representation eliminates redundant work while maintaining the same evidence available to the projection process. The cache must use appropriate invalidation or source-version keys so updated sales data cannot be replaced by stale content.

Options A and B modify the information supplied to Claude. Fewer sales chunks could remove relevant historical evidence, while truncating policy instructions could alter constraints or projection behavior. Option D introduces a model-capability change and therefore cannot guarantee unchanged accuracy without a comparative evaluation. Those interventions may be considered later, but only after representative regression testing establishes equivalence.

Study Guide references/topics: Cost profiling; prompt caching; retrieval caching; cache invalidation; accuracy-preserving optimization; model and context trade-offs.

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A new design cycle is warranted when the required change crosses an architectural boundary. Option D explicitly states that the existing architecture cannot satisfy the new requirements without redistributing component responsibilities or altering core contracts. Such changes may affect service ownership, orchestration, data flows, integration interfaces, security boundaries, failure handling, evaluation strategy, and operational accountability. They therefore require renewed discovery, impact analysis, design review, stakeholder approval, and regression planning.

Options A, B, and C are localized maintenance activities. Clarifying a runbook improves operational documentation but does not change the deployed system’s structure. Adjusting an alert threshold is a controlled operational tuning activity, provided the change remains within established monitoring policy. Editing customer-facing copy is similarly confined to the presentation or content layer. None of these changes inherently invalidates component contracts or architectural assumptions.

An architect should distinguish between iteration within an approved design and evidence that the design itself no longer supports the required outcome. Re-entering design for every minor adjustment creates unnecessary governance overhead; continuing local iteration after architectural assumptions have failed creates unmanaged technical and operational risk. Option D is the only signal that establishes a structural incompatibility.

Study Guide references/topics: Lifecycle feedback loops; design re-entry criteria; architectural significance; component responsibilities; interface contracts; controlled operational iteration.

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Authorization and compliance logging must remain enforceable application-layer controls. Option B requires the application to authenticate the requester, resolve the user’s role and entitlements, and deny the operation when the user lacks authority. A Skill can instruct Claude how to behave, but it is not a reliable security boundary and cannot replace identity-based access control enforced by the surrounding system.

Option A similarly belongs at the application layer. The application must create an authoritative audit event containing the requester, timestamp, Skill and model version, relevant input or content identifiers, generated output, review status, and retention classification. Audit evidence cannot depend on whether the model remembers or chooses to invoke a logging instruction.

Options C, D, and E describe reusable generation behavior that appropriately belongs within the approved Skill: house style, the standardized disclaimer, and the prohibited-language policy. The application may still validate their presence after generation, but the Skill remains the primary reusable content package.

The correct architecture separates behavioral guidance from deterministic enforcement. Skills supply domain instructions and templates; the application enforces identity, authorization, audit retention, data access, output validation, and consequential-action controls.

Study Guide references/topics: Skills versus application controls; authentication; authorization; compliance logging; deterministic guardrails; policy enforcement boundaries.

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The request is not yet design-ready because it lacks a defined failure, affected user journey, supported decision, target population, and measurable outcome. The architect should therefore facilitate structured discovery rather than prematurely selecting an AI pattern or model.

Discovery should identify where the current customer journey breaks down, who experiences the problem, what decision or task requires assistance, what data is available, and which metric would demonstrate improvement. The team can then establish baseline performance, target thresholds, operational constraints, risks, and explicit non-goals. Anthropic’s evaluation guidance states that an LLM application should begin with clearly defined success criteria and a method for measuring them. Define Success Criteria and Build Evaluations

Option A abandons the sponsor instead of helping translate a business concern into an actionable problem. Option B risks producing a generic demonstration unrelated to a validated customer need. Option C substitutes the architect’s assumptions for stakeholder evidence and may commit the organization to the wrong scope.

Competent delegation begins by defining the decision, intended outcome, authority boundaries, and target metric before determining which responsibilities should be assigned to Claude, conventional software, or human reviewers.

Study Guide references/topics: Structured discovery; delegation competency; problem definition; target metrics; decision support; measurable business outcomes.

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Control A directly constrains the outbound action surface. An allow-list limits which tools and destinations may receive data, while per-call review introduces an approval boundary before information leaves the controlled environment. These measures therefore address data exfiltration through outbound tool calls, making Option C correct. Anthropic’s security guidance recommends least privilege, narrowly scoped permissions, sandboxing, and limiting access to sensitive information and actions so that a compromised agent can cause minimal damage. Mitigate Jailbreaks and Prompt Injections

Control B addresses model-version drift. Running a stable adversarial evaluation set after each version change creates a consistent comparison baseline and detects regressions that might otherwise remain invisible during routine testing. This correctly maps Control B to silent quality drift, making Option E correct. Anthropic’s migration guidance repeatedly calls for workload reevaluation because instruction following, style, reasoning, and agent behavior can differ between versions. Model Migration Guide

Control A may limit the consequences of successful prompt injection, but it does not prevent or directly detect adversarial instructions in retrieved content. Control B measures behavioral performance but does not impose outbound authorization controls. Each control must therefore be mapped to the risk it most directly mitigates.

Study Guide references/topics: Tool governance; least privilege; exfiltration controls; adversarial evaluations; model-change regression testing; defense in depth.

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Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository’s normal clone, pull, review, and versioning workflow.

Anthropic’s scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.

Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.

Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.

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Threat assessment requires an inventory of what must be protected and who or what may threaten it. Asset identification should cover prompts, retrieved documents, credentials, model outputs, audit logs, tools, external systems, and sensitive business or personal data. Sensitivity classifications establish the potential confidentiality, integrity, safety, and availability impact of compromise. Threat actors and attack vectors must then be enumerated so likelihood and impact can be assessed against specific assets. Mitigation recommendations, residual-risk acceptance, ownership, and final documentation follow the analysis; they cannot be completed coherently before the threats are understood. Anthropic’s security transparency materials similarly emphasize maintaining threat models that reflect relevant attacker tactics and techniques. Anthropic Transparency Hub