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| Section | Weight | Objectives |
|---|---|---|
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Claude Models, Prompting & Context Engineering | 13% | - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs |
| Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions |
| Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools - Design authentication, authorization, and observability |
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NEW QUESTION # 96
You must present an architectural recommendation to deploy a Claude-based contract review assistant to a steering committee that includes the CFO, the general counsel, and the CIO. Each stakeholder cares about different aspects of the decision.
How should you structure the recommendation document?
Answer: B
Explanation:
A steering committee needs an answer-first recommendation that states the proposed architectural decision, the business outcome it supports, and the approval or action required. After establishing that common decision frame, the document should address each stakeholder's concerns separately.
The CFO requires implementation and operating costs, expected savings, consumption assumptions, financial exposure, and cost-control mechanisms. General counsel needs evidence concerning confidentiality, privilege, data handling, human legal review, explainability, retention, and regulatory risk. The CIO needs integration architecture, security boundaries, availability, scalability, operational ownership, technical dependencies, and lifecycle support.
Option A begins at an implementation depth unsuitable for the entire committee. Option B overweights the CFO's concerns and delays the rationale required by the other decision-makers. Option C ignores audience- specific responsibilities and forces every stakeholder to extract relevant information from an undifferentiated narrative.
The recommended structure is: decision and executive rationale, expected outcomes, accepted trade-offs, stakeholder-specific impact sections, major risks and controls, implementation implications, success metrics, and the requested governance decision. Technical appendices can preserve component-level detail without overwhelming the principal recommendation.
Study Guide references/topics: Executive communication; stakeholder differentiation; decision-first structure; financial, legal, and technology concerns; architectural recommendations.
NEW QUESTION # 97
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: C,E
Explanation:
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.
NEW QUESTION # 98
You are reviewing a peer's draft system prompt that contains contradictory instructions: one section says never to speculate beyond the supplied source, while another says to confidently fill in any gaps.
Which response is most appropriate?
Answer: B
Explanation:
The prompt contains mutually incompatible behavioral requirements. The architect should resolve the contradiction explicitly so the model receives one coherent grounding policy. If the application requires source-supported answers, the prompt should prohibit unsupported completion, permit an insufficient- evidence response, and require citations or supporting passages. Adding another priority instruction leaves the underlying conflict in place and creates ambiguity about when speculation is permitted. Increasing temperature makes behavior more variable rather than more correct. Retaining both instructions produces inconsistent outputs across requests and model versions. Anthropic recommends clear, direct, specific prompting and notes that conflicting project instructions can cause Claude to select among them unpredictably. Prompting best practices ; project-instruction troubleshooting
NEW QUESTION # 99
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Answer: A
Explanation:
Regression testing determines whether a change has broken behavior that previously satisfied the defined acceptance criteria. A stable reference set preserves known-good cases and enables the updated prompt to be compared with the prior baseline using consistent scoring rules. This makes Option B directly aligned with the described failure. Adversarial testing focuses on malicious or boundary inputs, such as prompt injection.
Smoke testing confirms that the system remains broadly available, but it does not measure whether previously correct model behavior has degraded. Integration testing verifies contracts and interactions among components and may not detect output-quality regressions caused specifically by a prompt change. Anthropic recommends task-specific evaluations that mirror real workloads, include edge cases, and measure performance against explicit success criteria throughout prompt iteration. Anthropic: Define success criteria and build evaluations
NEW QUESTION # 100
You are a solution architect designing a Claude-based assistant with access to 60 internal tools across multiple business domains. Loading every tool definition on every request increases token usage and time to first response.
Which design pattern best addresses this issue without sacrificing capability breadth?
Answer: B
Explanation:
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common tools and a discovery mechanism. When the task requires an additional capability, Claude searches the catalog and loads only the relevant tool definitions.
Anthropic's Tool Search mechanism implements this pattern through deferred loading. The context initially contains the search tool and any deliberately non-deferred tools; relevant definitions are expanded only when discovered. This reduces token consumption and helps maintain tool-selection accuracy in large catalogs.
Tool Search Tool
Option B adds a model call and still repeatedly processes all definitions, increasing latency and cost while potentially losing important schema details. Option C treats a larger context window as permission to waste context and does not address time-to-first-response overhead. Option D reduces capability breadth because the fixed five tools may be irrelevant to a given task.
The architect should provide clear tool and server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy, latency, and context savings across representative workflows.
Study Guide references/topics: Progressive discovery; deferred tool loading; MCP scaling; context optimization; tool-selection accuracy; on-demand capability loading.
NEW QUESTION # 101
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