MULTIPLE CHOICEYou are rolling out a standardized Claude Code configuration to an engineering team and must complete the planning steps before piloting the configuration.Which two steps must be completed BEFORE piloting the configuration with a small group of engineers? (Select two.)Each correct answer presents part of the solution.
Answer(s): A,D
Before a pilot begins, the architect must understand the workflows being enabled, the associated security boundaries, and the correct ownership of each configuration decision. Option D establishes this foundation. Organization-wide, non-overridable controls belong in managed configuration, whereas repository-specific MCP servers, shared permissions, and team subagents generally belong in project scope.Option A then converts those decisions into an initial project baseline that the pilot group can use. The baseline should identify approved MCP endpoints, minimum permission rules, denied operations, subagent responsibilities, configuration ownership, and observable success criteria. Anthropic distinguishes managed, user, project, and local scopes according to who they affect and whether they are shared with the team. Claude Code Configuration ScopesOptions B and C are broad-rollout activities and should not occur before pilot evidence is available. Option E is explicitly post-pilot: findings must first be collected before the configuration can be iterated and stabilized.The correct lifecycle is discovery, boundary definition, baseline design, limited pilot, evidence review, baseline refinement, documentation and training, followed by controlled expansion with a support and rollback process.Study Guide references/topics: Claude Code rollout planning; scope ownership; MCP baselines; permission design; pilot strategy; operational enablement.
FILL IN THE BLANKYou are architecting a sensitive healthcare application that uses Claude to analyze patient intake forms and medical records to identify potential drug interactions and contraindications. This application must comply with HIPAA regulations and your organization's data governance policies, which prohibit storing raw patient data in Claude's training data or on public infrastructure.When constructing your solution architecture, what critical feature of Claude must you enable to ensure patient data privacy and governance compliance?
Answer(s): A
Healthcare applications handling regulated data like HIPAA-protected patient information require explicit data privacy guarantees. Anthropic provides Claude API deployments with data isolation options and specialized infrastructure choices (such as private deployments) that ensure customer data is not retained, used for training, or stored on shared infrastructure. This is fundamentally different from public API usage where data retention policies may not meet healthcare compliance requirements. The solution architect must explicitly enable and document these privacy controls as part of governance compliance. Without this, the application violates HIPAA and data governance policy, creating legal and operational risk regardless of how well the prompts are designed.
A business sponsor has requested an AI solution to ''improve customer experience.'' The sponsor cannot articulate which customer journey is failing, which metric reflects the failure, or which decisions the AI should support. The sponsor is asking you to begin design work next week.Which delegation-competency action should you take first?
Answer(s): D
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 EvaluationsOption 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.
You are documenting an architectural decision to support future audit and onboarding.Which artifact is the strongest fit?
Answer(s): C
An Architecture Decision Record is designed to preserve a significant architectural choice in a concise, durable, and reviewable form. The context explains the problem, constraints, and forces that shaped the decision. The decision section records the selected approach. Alternatives demonstrate that other viable options were evaluated, while consequences identify benefits, costs, limitations, operational obligations, and residual risks. The date and authors establish accountability and historical sequence.This structure supports audit because a reviewer can trace why a model, integration pattern, retrieval architecture, security boundary, or deployment approach was chosen. It also supports onboarding by preventing new team members from interpreting intentional design choices as arbitrary implementation details.A slide deck may communicate a decision at a meeting but is weak as the sole authoritative record when it lacks written rationale. A code comment is too localized and may not capture system-level implications or rejected alternatives. A verbal conversation creates no durable evidence and depends entirely on individual memory.The ADR should be version-controlled, linked to affected requirements and implementation artifacts, assigned an owner, and reviewed when its assumptions or operating conditions materially change.Study Guide references/topics: Architecture Decision Records; decision context; alternatives; consequences; auditability; onboarding; lifecycle review.
MULTIPLE CHOICEYou are building an evaluation pipeline for a Claude-based deployment and must complete the specification steps before running the deployment against the dataset.Which two steps must be completed BEFORE running the deployment against the evaluation dataset? (Select two.)Each correct answer presents part of the solution.
Answer(s): B,D
The evaluation specification must exist before inference begins. Option D defines what the evaluation will measure and how results will be segmented. Representative cases measure normal workload performance, edge cases test boundary behavior, and adversarial cases examine safety, security, or robustness under hostile inputs.Option B then supplies the correctly curated and labeled dataset corresponding to those slices. Labels, expected outcomes, grading rubrics, provenance, and dataset versions must be established before the deployment generates outputs. Otherwise, evaluators risk modifying criteria after seeing results and introducing confirmation bias.Anthropic defines an evaluation as an input combined with grading logic used to measure success. Its evaluation guidance distinguishes tasks, trials, graders, traces, outcomes, and the harness that runs and aggregates them. Demystifying Evals for AI AgentsOption E occurs after model execution because outputs must exist before they can be scored. Option A follows scoring and aggregation. Option C is an iterative improvement step performed after failure cases become available, although rubric calibration may also be conducted on separate development data.Study Guide references/topics: Evaluation specification; metrics and slices; representative and adversarial datasets; labeling; scoring; aggregation; release gates.
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