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| Section | Objectives |
|---|---|
| Topic 1: Evaluation and Optimization | - Model and architecture optimization - Evaluation design and acceptance criteria |
| Topic 2: Stakeholder Engagement and Solution Lifecycle | - Solution lifecycle, handoff, adoption, and operational enablement - Stakeholder discovery and architectural trade-off communication |
| Topic 3: Solution Design | - Claude platform and solution architecture - Model, architecture, context, and deployment strategy selection |
| Topic 4: Enterprise Integration and Production | - Enterprise integration architecture and production readiness - Cost, latency, reliability, and operational design |
| Topic 5: Governance, Safety, and Risk | - Security, compliance, risk management, and governance - Safety controls and fail-closed system design |
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NEW QUESTION # 68
A Claude architect is leading the discovery phase for a new AI-powered customer service solution.
Which two activities are characteristic of structured discovery and requirement gathering for a Claude-based deployment? (Select two.)
Answer: B,C
Explanation:
Structured discovery begins by eliciting constraints from the people who own the business outcome, operate the process, manage risk, and will use or support the deployed system. Stakeholder workshops in Option A identify measurable expectations concerning accuracy, latency, throughput, cost, privacy, compliance, escalation, and acceptable failure behavior before the solution is scoped.
Option D converts those expectations into production gates. Success criteria must be explicit enough to evaluate empirically, while failure thresholds must determine when the design requires remediation, human review, or rejection. Anthropic's evaluation guidance states that successful LLM applications begin with defined success criteria and evaluations that measure performance against them. It characterizes strong criteria as specific, measurable, achievable, and relevant. Define Success Criteria and Build Evaluations Option B prematurely selects a model before the workload profile is known. Option C can supplement discovery, but reaction-driven prototyping cannot replace documented requirements and acceptance criteria.
Option E postpones essential constraints until architectural commitments may already have been made.
The discovery output should create traceability from stakeholder objectives to requirements, evaluation metrics, architecture decisions, and deployment gates.
Study Guide references/topics: Structured discovery; stakeholder workshops; nonfunctional requirements; success criteria; failure thresholds; production readiness gates.
NEW QUESTION # 69
You are compiling factors that should drive the choice between Model Context Protocol (MCP), direct API integration, and agent-to-agent handoff.
Which two factors belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: B,D
Explanation:
The interaction model and portability requirement are architectural decision drivers. MCP is appropriate when tools or data sources must be exposed through a standardized interface reusable across compatible AI clients.
Direct API integration is often preferable for a narrow, stateless, latency-sensitive call where the application already owns the integration logic. Agent-to-agent handoff is better suited to stateful or longer-running delegation between independently responsible agents. Documentation quality and team familiarity influence implementation effort but do not determine the correct integration pattern. Encryption in transit is a mandatory security property for production integration rather than a differentiating architectural requirement; each viable approach must provide it. Anthropic distinguishes direct Messages access from stateful managed- agent infrastructure, while MCP supplies a standardized tool integration layer. Claude Platform documentation
NEW QUESTION # 70
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Answer:
Explanation:
Explanation:
Persist the validated output for downstream consumption and audit - Output Handling Order prompt sections so cacheable content appears before per-request content - Prompt Construction Summarize prior conversation history when full history is no longer needed - Input Preparation Validate the model's structured output against the expected schema - Output Handling Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching
NEW QUESTION # 71
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: D
Explanation:
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common common 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 implements this pattern through deferred loading. The context initially contains the search search 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. ToolTool Search Tool Option B adds a model call and still repeatedly processes all definitions, increasing latency latency 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 server server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy
NEW QUESTION # 72
You are evaluating model-selection claims used by a peer team.
For each statement, select yes if the statement is generally true. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, No, Yes, No
More capable reasoning configurations generally require greater computation, produce higher latency, and may consume larger token or rate-limit allocations than lighter alternatives. This makes the first statement generally accurate, although actual performance must be measured under the organization's specific provider and service tier.
Representative workload testing is essential because generic benchmark rankings do not establish performance on the organization's prompts, data, edge cases, tools, or required output format. Anthropic recommends benchmark tests specific to the use case, using actual prompts and data, followed by comparison of accuracy, quality, edge-case behavior, and cost. Choosing the Right Model The strongest model is not automatically the correct production choice. Model selection must balance capability, speed, cost, throughput, and operational constraints. Tiered routing implements this principle by serving predictable requests with an efficient model and escalating difficult cases to a more capable model.
Anthropic documents multi-model strategies in which lower-cost workers handle most traffic while harder decisions are escalated.
Recency is also not a valid selection criterion. A newly announced model must pass the workload's existing evaluation, security, latency, and cost gates before replacing a validated production model.
Study Guide references/topics: Model trade-offs; representative evaluations; tiered routing; escalation architectures; cost-capability optimization; model migration validation.
NEW QUESTION # 73
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