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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost |
| Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs |
| Solution Design & Architecture | 17% | - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies |
| Integration | 19% | - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems |
| Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
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NEW QUESTION # 102
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: B
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 # 103
You are designing the prompt for a ticket-triage classifier with thirty well-defined categories and clear category descriptions in the prompt.
Which technique is most appropriate as the starting point?
Answer: C
Explanation:
A clearly defined closed-set classification task should begin with a concise zero-shot prompt containing the permitted categories, unambiguous category descriptions, and the required output structure. This provides a simple, inexpensive baseline that can be measured against a representative evaluation set. Chain-of-thought prompting adds unnecessary latency and token usage when the routing decision does not require substantive multi-step reasoning. Permitting Claude to invent categories would violate the closed-set contract and complicate downstream automation. Omitting descriptions would increase ambiguity among potentially overlapping labels. If evaluation later reveals persistent confusion between particular categories, the architect can introduce targeted examples or refine the definitions. Prompt complexity should be added in response to measured failure patterns, not assumed to be necessary before establishing baseline performance. Anthropic:
Prompt engineering overview
NEW QUESTION # 104
You are compiling a diagnostic toolkit for Claude Code operational issues.
Which two diagnostic actions belong in the toolkit? (Select two.)
Each correct answer presents a complete solution.
Answer: C,E
Explanation:
A minimal reproduction and MCP status inspection are foundational diagnostic actions because they produce direct, interpretable evidence. A minimal reproduction removes unrelated prompts, tools, settings, hooks, and repository state while changing one variable at a time. This determines whether the failure follows a particular configuration, server, command, model request, or project artifact. It also creates a reproducible evidence package if escalation later becomes necessary.
MCP inspection addresses a common Claude Code failure domain. `claude mcp list` displays configured servers and their health states, including connected, authentication-required, pending-approval, and failed-to- connect conditions. `claude mcp get < name > ` provides server-specific details, while `/mcp` exposes status within an interactive session. These checks distinguish model behavior from transport, authentication, configuration, and workspace-trust failures.
Increasing temperature reduces repeatability and introduces additional variation, making root-cause isolation more difficult. Filing a support ticket before collecting local evidence reverses the appropriate diagnostic sequence. An immediate rollback may eventually be used as a controlled version comparison, but it changes the environment before a baseline has been captured and is therefore not a primary toolkit action.
Study Guide references/topics: [Managing and diagnosing MCP servers](https://docs.anthropic.com/en/docs
/claude-code/mcp); minimal reproducible cases; controlled-variable troubleshooting; authentication and connection-state diagnostics.
NEW QUESTION # 105
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 # 106
You are building an ethics-review checklist for deployments supported by artificial intelligence.
Which two checks belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: B,D
Explanation:
An ethics review must examine unsupported generalizations, stereotyping, disparate treatment, and other conclusions about people that are not justified by the evidence. It must also preserve identifiable human accountability for consequential decisions; responsibility cannot be transferred to a probabilistic system.
Licensing is a legal and commercial-control question, while latency and throughput are operational requirements. Both remain important, but neither is the central ethical test presented here. Restricting review to low-confidence or threshold-triggering outputs is also inadequate because models can express harmful or biased conclusions with high confidence. Ethical review should cover representative populations, protected and intersectional groups, high-impact outcomes, override mechanisms, transparency, and channels for challenge or appeal.
Study Guide references/topics: Bias and unsupported generalization; human accountability; consequential decisions; ethical oversight.
NEW QUESTION # 107
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