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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Implement iterative improvement pipelines |
| Topic 3: 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 |
| Topic 4: Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling |
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering |
| Topic 6: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs |
| Topic 7: Integration | 19% | - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools |
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NEW QUESTION # 32
You are reviewing an integration specification for security gaps.
Which two findings constitute valid security gaps in the specification? (Select two.) Each correct answer presents a complete solution.
Answer: A,E
Explanation:
Credentials must never be supplied as prompt content. Prompts can be retained in traces, debugging output, caches, or logs, and model output may inadvertently expose included secrets. Credentials should instead be resolved server-side through an approved secret store or delegated authentication mechanism. Option C is also a serious gap because post-response RBAC occurs after unauthorized data has already entered model context.
Authorization must be enforced before retrieval and prompt construction. Options A, D, and E describe appropriate controls: least-privilege service identities, per-user scoped OAuth, and encrypted transport.
Anthropic's prompt-leak guidance recommends excluding unnecessary sensitive information, while Claude Code security relies on permission-based enforcement rather than natural-language instructions alone. Reduce prompt leak
NEW QUESTION # 33
You are assessing a Claude-based system whose dominant risk is silent quality drift on safety-relevant outputs after a model-version upgrade.
Which assessment activity most directly addresses this risk?
Answer: A
Explanation:
A stable adversarial regression set provides a controlled basis for detecting behavioral change between model versions. Version-attributed results show whether the proposed upgrade improves, preserves, or degrades performance on the same safety-relevant cases. The evaluation must run before promotion so a regression can block deployment rather than becoming a production incident. Randomly replacing the test inputs removes comparability and makes score changes difficult to attribute. Disabling evaluation or relying on complaints converts a preventable release-control failure into reactive incident handling. Anthropic characterizes regression evaluations as tests of whether an agent still handles tasks it previously completed and recommends maintaining them continuously to detect backsliding. Demystifying evals for AI agents
NEW QUESTION # 34
You are designing a human-in-the-loop validation workflow for a new Claude-based deployment and must complete the design steps before piloting the workflow.
Which two steps must be completed BEFORE piloting the workflow with a representative subset of traffic?
(Select two.)
Each correct answer presents part of the solution.
Answer: B,C
Explanation:
The workflow must first identify where human judgment is necessary. Option E applies risk-based oversight by examining each decision's impact and reversibility. High-impact, irreversible, externally binding, privacy- sensitive, or legally consequential actions require stronger review than low-risk draft generation or easily reversible recommendations.
After locating those control points, Option A defines how work reaches reviewers. The sampling strategy should specify whether review is universal, random, risk-triggered, confidence-based, or stratified. Escalation criteria should cover policy violations, uncertain outputs, conflicting evidence, sensitive data, failed validation, and cases exceeding the reviewer's authority.
Anthropic recommends human confirmation for decisions with meaningful real-world consequences and actions requiring affirmative consent. Computer Use Security Considerations Option B necessarily follows the pilot because findings must exist before design iteration. Reviewer onboarding and operational documentation in Options C and D are important readiness activities, but they follow the core design decisions being tested by this item: where oversight is required and how cases are sampled and escalated.
The completed pilot plan should subsequently include trained reviewers, documented procedures, queue capacity, SLAs, audit evidence, and measured inter-reviewer agreement.
Study Guide references/topics: Human-in-the-loop design; impact and reversibility; oversight points; sampling; escalation criteria; consequential decisions.
NEW QUESTION # 35
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 # 36
You are choosing the level of detail for an implementation guide. The audience is a delivery team that will build the deployment.
Which guidance composition best serves them?
Answer: B
Explanation:
A delivery team needs implementation-level direction rather than an executive architecture summary.
Component responsibilities establish ownership and boundaries, while interface contracts define schemas, accepted inputs, outputs, errors, and behavioral expectations. Sequence diagrams show how components interact across dominant and failure flows. Configuration tables with defaults prevent inconsistent assumptions between environments. Operational runbooks ensure that deployment, recovery, monitoring, and routine maintenance are addressed during implementation rather than improvised after release. Options B, C, and D omit one or more elements required to translate architectural intent into a buildable and supportable system. The guide should enable normal delivery work without continuous dependence on undocumented knowledge held by the architect.
Study Guide references/topics: Implementation documentation; component contracts; sequence diagrams; configuration management; operational readiness.
NEW QUESTION # 37
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