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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
| Topic 2: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques |
| Topic 3: Integration | 19% | - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection |
| Topic 6: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions |
| Topic 7: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance - Implement guardrails and safety controls |
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NEW QUESTION # 86
A technical team is cataloguing risks specific to Claude's use in a document-grounded Q & A system.
Which two items represent failure modes intrinsic to LLM-based systems rather than generic software defects? (Select two.)
Answer: B,C
Explanation:
Over-refusal and hallucination are characteristic model-behavior risks. An overly broad safety response can reject a legitimate request even when the surrounding application operates correctly. A plausible but unsupported answer is a groundedness failure arising from probabilistic generation and inadequate source adherence. These risks require model-specific evaluations, grounding instructions, citations, verification, and calibrated fallback behavior. Expired certificates, database timeouts, and load-balancer misconfiguration are conventional distributed-system failures that should be addressed through infrastructure monitoring, retries, health checks, and configuration management. Distinguishing these categories matters because ordinary software remediation will not resolve behavioral failures, while prompt changes will not repair infrastructure.
Anthropic's hallucination guidance recommends auditable citations and supporting evidence for generated claims. Reducing hallucinations
NEW QUESTION # 87
A revenue projection assistant has missed its monthly cost target by 38 percent. Profiling shows three contributors: a 6,000-token policy preamble repeated on every call (45 percent of cost), retrieval of historical sales chunks averaging 3,000 tokens per call (30 percent), and inference on a flagship-tier model (25 percent).
Stakeholders require that projection accuracy remain unchanged.
Which two optimizations should you sequence first to reduce cost without affecting accuracy? (Select two.) Each correct answer presents part of the solution.
Answer: C,E
Explanation:
The required sequence must reduce repeated computation without changing the information or model capability used to generate projections. Prompt caching the static 6,000-token policy preamble directly addresses the largest cost contributor while preserving the complete instruction set. Anthropic states that cache reads cost substantially less than uncached input tokens, making repeated, stable prompt prefixes an ideal caching target. Prompt Caching Caching frequently reused historical-sales chunks applies the same principle to the retrieval layer. When identical, version-controlled chunks are repeatedly fetched and supplied to the model, caching their retrieval or reusable representation eliminates redundant work while maintaining the same evidence available to the projection process. The cache must use appropriate invalidation or source-version keys so updated sales data cannot be replaced by stale content.
Options A and B modify the information supplied to Claude. Fewer sales chunks could remove relevant historical evidence, while truncating policy instructions could alter constraints or projection behavior. Option D introduces a model-capability change and therefore cannot guarantee unchanged accuracy without a comparative evaluation. Those interventions may be considered later, but only after representative regression testing establishes equivalence.
Study Guide references/topics: Cost profiling; prompt caching; retrieval caching; cache invalidation; accuracy- preserving optimization; model and context trade-offs.
NEW QUESTION # 88
A document analysis service processes legal filings averaging 80,000 tokens each. Each filing is queried by attorneys an average of 14 times during a case. The current architecture sends the full filing on every query.
The CFO has asked you to reduce per-query costs while preserving response quality. The security officer requires that filing contents not be stored outside Fabrikam's tenancy.
Which optimization approach should you recommend?
Answer: A
Explanation:
The 80,000-token filing is a large, stable prefix reused across fourteen requests, making prompt caching the strongest optimization. The first request creates the cache; subsequent identical-prefix requests use lower- priced cache reads while retaining the complete filing, preserving the evidence available to Claude. Anthropic documents cache reads at a fraction of standard input cost and confirms that caches are isolated by organization and, on supported platforms, by workspace. Cached representations remain in memory rather than persistent storage. Options A and C may omit context through summarization or retrieval and therefore require additional quality evaluation. Option D lowers unit cost but may reduce reasoning quality. The design must preserve prefix identity, choose an appropriate TTL, and verify the tenancy configuration.
Study Guide references/topics: Prompt caching ; cache isolation; stable prefixes; long-document analysis; cost optimization.
NEW QUESTION # 89
You are defining escalation criteria for ambiguous cases in an automated workflow.
Which set of criteria best supports consistent escalation?
Answer: D
Explanation:
Option D defines observable and repeatable escalation triggers. Confidence thresholds identify cases where the system lacks sufficient certainty. High-impact categories ensure that legally, financially, medically, or operationally consequential decisions receive human attention regardless of nominal confidence. Classifier- generated ambiguity flags add an independent control, while an explicit user request preserves the individual' s ability to obtain review. These triggers should be documented, tested, logged, and mapped to reviewer authority and response-time objectives. Options A and C make escalation discretionary and inconsistent.
Option B is purely reactive because the potentially harmful automated action has already occurred. A sound workflow escalates before consequential execution and records the reason, evidence, reviewer, decision, and outcome.
Study Guide references/topics: Human-in-the-loop governance; escalation thresholds; ambiguity detection; consequential decisions; review auditability.
NEW QUESTION # 90
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: A
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 # 91
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