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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines |
| Topic 2: Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks |
| Topic 4: 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 |
| Topic 5: Integration | 19% | - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 6: 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 |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
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NEW QUESTION # 60
You are responding to a stakeholder request that adds significant scope to a deployment already in flight.
Which response best aligns expectations while respecting the stakeholder's underlying need?
Answer: A
Explanation:
Option C preserves both transparency and stakeholder agency. The architect acknowledges the underlying business need while making the delivery consequences explicit. Presenting controlled alternatives converts an unbounded request into a governed decision: defer the capability, exchange it for existing scope, or accept a later release date. The selected option should be documented with revised success criteria, dependencies, risk, cost, ownership, and approval. Concealing the request prevents accurate planning and creates unmanaged delivery risk. Rejecting it without exploring alternatives ignores legitimate stakeholder value. Quietly absorbing additional work produces scope creep and makes later delays appear unexpected. Effective lifecycle communication does not promise every requested capability; it presents the trade-offs necessary for an informed decision.
Study Guide references/topics: Scope governance; expectation alignment; transparent trade-offs; change control; stakeholder decision rights.
NEW QUESTION # 61
You are compiling team-setup practices for a Claude Code rollout across an engineering organization.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Project scope is designed for repository-specific configuration shared through version control, including MCP server definitions, project permissions, hooks, and team instructions. This gives every contributor a consistent baseline while keeping changes reviewable. Managed configuration is appropriate for mandatory security and compliance controls because administrators can deploy it centrally and prevent lower scopes from overriding it. Local scope is machine-specific and unsuitable for organization-wide enforcement. User scope follows one engineer across projects, so it cannot guarantee that an entire team receives identical MCP definitions.
Personal editor preferences belong in user or local configuration, not project scope, unless the team has explicitly standardized them. Anthropic's settings hierarchy distinguishes managed, project, local, and user responsibilities. Claude Code settings
NEW QUESTION # 62
You are defining transparency practices for a customer-facing assistant whose responses are materially shaped by AI.
Which transparency practice most directly supports responsible deployment?
Answer: C
Explanation:
Responsible deployment requires users to receive an accurate representation of the system with which they are interacting. Option C provides that transparency while also establishing a practical escalation path.
Disclosure should be proportionate to the material role AI plays, expressed in language appropriate to the audience, and aligned with organizational policy and applicable regulatory obligations.
A documented human-contact route is important when the assistant cannot resolve an issue, produces a disputed result, encounters a high-impact exception, or handles a matter requiring human authority. Anthropic' s Responsible Scaling Policy recognizes escalation to human reviewers as an appropriate safeguard for edge cases and situations requiring human judgment. Responsible Scaling Policy Options A and D intentionally conceal material AI involvement from affected users, weakening informed decision-making, auditability, and trust. Option B is also unsuitable because transparency does not require revealing proprietary prompts, confidential controls, or internal security mechanisms; however, it does require an honest explanation of AI involvement and the system's operational role.
The correct design combines disclosure, understandable limitations, human escalation, and records demonstrating that the organization's transparency requirements are consistently applied.
Study Guide references/topics: AI transparency; informed user interaction; human escalation; responsible deployment; disclosure policy; operational accountability.
NEW QUESTION # 63
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: D,E
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 # 64
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 # 65
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