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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Regulatory and compliance requirements - Human-in-the-loop validation - AI safety and guardrails - Security and risk management |
| Claude Models, Prompting & Context Engineering | 13% | - Guardrails - System prompts and prompt templates - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Context window optimization |
| Solution Design & Architecture | 17% | - Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - End-to-end architecture design - Alignment with business value, cost, performance, and SLAs - Architectural patterns
|
| Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - A/B testing - Cost and performance optimization - Evaluation framework design - Production monitoring and optimization - System issue diagnosis |
| Integration | 19% | - Enterprise system integration - RAG pipeline design
|
| Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Debugging and operational issue resolution - Claude tooling configuration for teams - Developer enablement |
| Stakeholder Communication & Lifecycle Management | 14% | - Solution lifecycle management - Service-level agreements - Stakeholder management - Discovery and requirements gathering - Architecture documentation - Communicating architectural decisions |
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NEW QUESTION # 75
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,E
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 # 76
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: A
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 # 77
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: B
Explanation:
Option C addresses both components responsible for the excessive cost. Anthropic prompt caching allows stable, repeatedly submitted prompt material-such as system instructions, tool definitions, and reusable background information-to be placed in a consistent prefix. After that prefix is written to the cache, qualifying subsequent requests can reuse it at the lower cache-read cost instead of repeatedly processing the same content at the standard input-token rate.
Retrieval must be optimized separately. Supplying an entire document collection or excessively deep search results increases cost, consumes context capacity, and may reduce answer quality by surrounding the relevant evidence with distracting material. Retrieval should select the smallest set of authoritative passages that provides sufficient evidence for the current query. This typically requires relevance scoring, deduplication, metadata filtering, reranking, and explicit token-budget limits.
Increasing retrieval depth or expanding the repeated system prompt directly worsens the identified cost driver.
Moving every request to a heavier model changes the unit economics but does not correct inefficient context construction. The recommended optimization therefore combines prefix caching with query-specific context pruning, followed by evaluation to confirm that reduced context does not lower task accuracy.
Study Guide references/topics: [Prompt caching](https://docs.anthropic.com/en/docs/build-with-claude
/prompt-caching); [effective context engineering] (https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents); retrieval precision; token-cost analysis; cache-prefix stability.
NEW QUESTION # 78
A Claude architect observes that after a recent model-version upgrade, grounded responses began including claims not supported by the retrieved source documents.
Which mitigation is most directly targeted at this failure mode?
Answer: A
Explanation:
The observed failure is a groundedness defect: the response contains claims that cannot be traced to retrieved evidence. Option A directly constrains generation to the supplied sources, requires visible attribution, and verifies that each material claim has supporting text. Unsupported claims should be removed, qualified, or replaced with an explicit statement that the available sources are insufficient. Regression evaluation in Option B may detect the failure but does not itself prevent unsupported claims. OAuth addresses identity and authorization, while input classifiers address malicious or unsafe retrieved content; neither verifies factual support. Anthropic recommends citations, supporting quotations, and post-generation claim verification to reduce hallucinations in document-grounded applications. Reducing hallucinations
NEW QUESTION # 79
The platform team at Trenova Systems, Inc. needs to reduce per-query cost and p95 latency for a high-volume Claude pipeline without degrading output quality on the core use case.
Which two optimizations directly target both cost and latency simultaneously? (Select two.)
Answer: B,E
Explanation:
Prompt caching reduces repeated input processing when a stable system prefix is reused, directly lowering input cost and processing latency. Model routing provides the second optimization: routine cases can use a faster, lower-cost model, while difficult cases retain access to the more capable model. The routing policy must be validated against representative evaluations to confirm that quality remains within the required threshold. Increasing max_tokens can increase output cost and generation time. Retrieving the complete corpus and adding examples to every request both enlarge the input, worsening latency and token consumption. Anthropic identifies prompt caching as a cost-and-latency optimization and recommends selecting a model according to the workload's quality, speed, and economic requirements. Prompt caching ; reducing latency
NEW QUESTION # 80
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