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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
| Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions |
| Integration | 19% | - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools |
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NEW QUESTION # 14
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: D
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 # 15
The engineering lead at Trenova Systems, Inc. is evaluating two proposals for improving developer workflows using Claude-assisted tooling. Proposal A adds Claude Code to the IDE for inline code generation and review. Proposal B routes all code-generation requests through a shared Slack bot without IDE integration.
Which two observations most accurately evaluate these proposals against workflow-improvement objectives?
(Select two.)
Answer: D,E
Explanation:
Workflow improvement depends on where assistance is delivered, not solely on the underlying model. IDE integration makes code, selections, diagnostics, diffs, and repository context available at the point where developers work. This reduces context switching and avoids repeatedly transferring code between systems.
Routing every request through Slack introduces an additional interaction surface, separates assistance from development context, and increases friction. A Slack record may support auditability, but it does not outweigh the stated productivity objective, and equivalent audit data can be captured through approved telemetry and repository history. Notifications do not inherently improve engineering velocity. Anthropic's Claude Code integrations are designed to place agentic coding capabilities directly within terminal and IDE workflows.
NEW QUESTION # 16
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,D
NEW QUESTION # 17
You are a solution architect evaluating candidate use cases for a Claude-based program.
For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
Otherwise, select No.
Answer:
Explanation:
Explanation:
Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No Long-context contract review with structured clause extraction and deviation flagging - Yes Replacing a vector index for semantic retrieval over a multi-million-document corpus - No Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.
NEW QUESTION # 18
You are selecting the documentation set that should accompany a Claude-based deployment at handoff.
Which set is most complete?
Answer: B
NEW QUESTION # 19
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