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| Section | Weight | Objectives |
|---|---|---|
| Stakeholder Communication & Lifecycle Management | 14% | - Solution lifecycle management - Stakeholder management - Service-level agreements - Communicating architectural decisions - Discovery and requirements gathering - Architecture documentation |
| Evaluation, Testing & Optimization | 16% | - Production monitoring and optimization - Evaluation framework design - System issue diagnosis - A/B testing - Evaluation metrics and datasets - Cost and performance optimization |
| Integration | 19% | - RAG pipeline design
- Claude integration mechanisms
|
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams |
| Solution Design & Architecture | 17% | - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - End-to-end architecture design - Decomposition techniques for complex problem solving - Architectural patterns
|
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Claude model selection and trade-offs - Guardrails - Context window optimization - Prompt reuse and context engineering strategies |
| Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Regulatory and compliance requirements - Human-in-the-loop validation - Security and risk management - AI safety and guardrails |
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NEW QUESTION # 79
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: C,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 # 80
You are designing a content moderation classifier that processes high volumes of user-generated comments under a tight per-message latency budget using well-defined classification labels.
Which model selection best aligns with the workload?
Answer: C
Explanation:
Haiku is the appropriate starting point because the workload is high-volume, latency-sensitive, and based on a stable closed set of moderation labels. These characteristics favor a fast, cost-efficient model capable of consistent classification without incurring the additional inference time and expense associated with deeper reasoning.
Anthropic's model-selection guidance requires architects to balance capability, speed, and cost rather than automatically selecting the most capable model. Its content-moderation guidance specifically identifies Haiku as a cost-effective option for processing moderation workloads at substantial scale. Choosing the Right Model, Content Moderation Opus is disproportionate to a routine closed-set classification problem. Sonnet may become justified if evaluation demonstrates that Haiku fails materially on complex policy distinctions, multilingual ambiguity, or adversarial edge cases, but it should not be selected merely because it is larger. Enabling extended thinking on every request would further increase latency and token consumption without evidence that the additional reasoning improves the defined success metrics. The correct architectural practice is to establish a representative moderation evaluation set, validate Haiku against accuracy and safety thresholds, and escalate only the cases that genuinely need deeper reasoning.
Study Guide references/topics: Model selection; capability-latency-cost trade-offs; classification workloads; evaluation-driven routing; moderation architecture.
NEW QUESTION # 81
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: A
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 # 82
You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Answer:
Explanation:
Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
NEW QUESTION # 83
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
Answer: D
NEW QUESTION # 84
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