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| Section | Objectives |
|---|---|
| Enterprise Integration and Production | - Enterprise integration architecture and production readiness - Cost, latency, reliability, and operational design |
| Governance, Safety, and Risk | - Safety controls and fail-closed system design - Security, compliance, risk management, and governance |
| Stakeholder Engagement and Solution Lifecycle | - Stakeholder discovery and architectural trade-off communication - Solution lifecycle, handoff, adoption, and operational enablement |
| Solution Design | - Claude platform and solution architecture - Model, architecture, context, and deployment strategy selection |
| Evaluation and Optimization | - Evaluation design and acceptance criteria - Model and architecture optimization |
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NEW QUESTION # 77
You are operating an interactive assistant whose dominant performance constraint is per-turn latency. Quality on routine turns is already acceptable.
Which configuration adjustment most directly improves latency without disproportionately damaging quality?
Answer: D
Explanation:
Option B targets two major contributors to per-turn latency: retrieved-context size and repeated prompt processing. Reducing retrieval to an empirically validated top- k limits the tokens Claude must process while preserving the passages that historically provide sufficient answer coverage. Caching the stable system- prompt prefix avoids repeatedly processing identical instructions.
Anthropic's latency guidance recommends choosing an appropriate model and reducing unnecessary input and output tokens. Its prompt-caching documentation explains that caching reusable prompt prefixes can reduce both repeated processing cost and latency. Reducing Latency , Prompt Caching The retrieval depth must be selected through evaluation rather than arbitrary truncation. The team should compare answer quality, retrieval recall, latency, and failure rates across candidate values and retain escalation logic for complex queries requiring deeper retrieval.
Option A removes an applicable latency optimization. Option C maximizes context consumption regardless of demonstrated benefit. Option D deliberately selects the slowest model class even though routine-turn quality already meets requirements.
Study Guide references/topics: Latency optimization; top- k retrieval; prompt caching; token reduction; quality-latency evaluation; adaptive retrieval.
NEW QUESTION # 78
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 # 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: A,B
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
You are supporting an engineer whose newly added MCP server does not appear in their Claude Code session.
Which diagnostic step should be tried first?
Answer: A
Explanation:
The first diagnostic action must establish whether Claude Code has loaded the MCP server configuration and whether it can connect to the registered endpoint. The official management command is claude mcp list; within Claude Code, /mcp can also be used to inspect server status. The listing distinguishes registered and connected servers from configurations that require authentication, approval, or connection troubleshooting.
Connect Claude Code to Tools via MCP
If the server was added or its configuration changed after the current session began, reconnecting or restarting the relevant Claude Code session is a proportionate next step. This ensures that configuration state and available tool definitions are refreshed before more intrusive troubleshooting begins. If the server is listed but unhealthy, the engineer can then inspect it with claude mcp get < name > and investigate transport, command path, credentials, workspace approval, or endpoint connectivity.
Options A, B, and D are operationally unsound because they discard working capabilities or environments before confirming the basic registration state. Disabling all MCP servers eliminates useful diagnostic comparisons; changing product families abandons the relevant configuration; reinstalling the operating system is grossly disproportionate and risks destroying evidence needed for diagnosis.
Study Guide references/topics: Claude Code environment configuration; MCP registration; server-status inspection; session refresh; targeted operational troubleshooting.
NEW QUESTION # 81
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 # 82
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