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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - System prompts and prompt templates - Guardrails - Context window optimization - Prompt reuse and context engineering strategies |
| Topic 2: Integration | 19% | - Authentication and authorization analysis - Claude integration mechanisms
- RAG pipeline design
|
| Topic 3: Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - Evaluation metrics and datasets - System issue diagnosis - Evaluation framework design - A/B testing - Production monitoring and optimization |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Stakeholder management - Discovery and requirements gathering - Solution lifecycle management - Architecture documentation - Service-level agreements |
| Topic 5: Solution Design & Architecture | 17% | - Architectural patterns
- Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs - End-to-end architecture design |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - Developer enablement - Claude tooling configuration for teams - Debugging and operational issue resolution - AI-assisted developer workflows |
| Topic 7: Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Security and risk management - Human-in-the-loop validation - AI safety and guardrails - Regulatory and compliance requirements |
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NEW QUESTION # 40
An engineering organization is adopting Claude Code across 200 developers. A team lead proposes that AI- generated pull requests bypass standard code review for changes under 50 lines because small changes are considered low risk and review capacity is constrained.
Which two Diligence-competency objections should you raise? (Select two.) Each correct answer presents part of the solution.
Answer: B,C
Explanation:
AI-generated code requires verification through the same controlled engineering workflow as human-authored changes. Removing review eliminates the gate that can detect incorrect assumptions, security defects, missing tests, and unintended behavior. Line count is not a defensible risk measure: a one-line authorization change, dependency alteration, or command-execution defect can be more consequential than a large documentation update. Review requirements should instead be determined by affected components, privilege, data sensitivity, reversibility, test coverage, and deployment impact. Expanding capacity may be operationally useful but is not the central diligence objection. Raising the threshold retains the same flawed proxy, while rejecting all AI-generated changes discards legitimate productivity benefits. Anthropic states that users remain responsible for reviewing proposed code and commands. Claude Code security
NEW QUESTION # 41
A pilot AI assistant for procurement specialists shows 89 percent first-response acceptance, but follow-up surveys reveal that specialists frequently override the assistant's vendor recommendations after considering criteria the assistant did not evaluate. The pilot owner wants to ship the assistant unchanged because of the strong acceptance rate.
Which two Discernment-competency observations should you raise BEFORE approving the launch? (Select two.)
Answer: B,C
Explanation:
The assistant is making recommendations without considering information that specialists regard as material.
That is a direct input-scope and requirements gap, not merely a presentation issue. The 89 percent acceptance metric is also incomplete because initial acceptance does not reveal whether recommendations remain correct after users apply the omitted criteria. Production readiness requires outcome-focused evaluation, including final recommendation agreement, override reasons, financial impact, supplier risk, and performance across representative procurement scenarios. Options B and E raise possible concerns, but the scenario supplies no evidence that response rate or pilot duration is the demonstrated deficiency. Option A incorrectly treats a single proxy metric as sufficient. Anthropic recommends multidimensional, task-specific success criteria aligned with the actual user outcome. Define success criteria and evaluations
NEW QUESTION # 42
A healthcare organization is evaluating two Claude-powered AI architectures for a clinical documentation assistant. Architecture X produces higher output quality scores but costs $0.18 per documentation session and averages 4.2 seconds per response. Architecture Y scores slightly lower on quality metrics but costs $0.09 per session and averages 2.1 seconds per response. The stated SLA requires responses under 3 seconds, and the annual volume is projected at 2 million documentation sessions.
Which evaluation approach correctly applies business value pillar analysis to this decision?
Answer: B
Explanation:
Architecture X fails a stated production gate: its 4.2-second average exceeds the required three-second response SLA. It should therefore not remain a qualifying architecture unless it is redesigned and retested before selection. A promise to optimize after deployment is not evidence of compliance.
Architecture Y meets the latency requirement at 2.1 seconds. Its annual inference cost is $180,000 at two million sessions, compared with $360,000 for Architecture X-a difference of $180,000 annually. However, cost alone does not establish suitability. The organization must determine whether Y's lower quality score causes material omissions, correction effort, clinician dissatisfaction, documentation risk, or reduced workflow productivity.
Option C correctly treats the SLA as a threshold and then analyzes the remaining architecture across cost, efficiency, quality, and productivity. Option A ignores clinical quality. Option B disregards an explicit SLA failure. Option D may eventually be viable, but it introduces routing complexity and leaves unanswered how complex cases are detected, whether X's latency becomes acceptable for them, and how both paths are evaluated.
The final choice should be gated by clinical-quality evaluation, human review requirements, safety metrics, operational latency, and total annual cost.
Study Guide references/topics: Business-value pillars; performance gates; solution cost; workflow efficiency; quality trade-offs; healthcare architecture evaluation.
NEW QUESTION # 43
You must present an architectural recommendation to deploy a Claude-based contract review assistant to a steering committee that includes the CFO, the general counsel, and the CIO. Each stakeholder cares about different aspects of the decision.
How should you structure the recommendation document?
Answer: A
Explanation:
A steering committee needs an answer-first recommendation that states the proposed architectural decision, the business outcome it supports, and the approval or action required. After establishing that common decision frame, the document should address each stakeholder's concerns separately.
The CFO requires implementation and operating costs, expected savings, consumption assumptions, financial exposure, and cost-control mechanisms. General counsel needs evidence concerning confidentiality, privilege, data handling, human legal review, explainability, retention, and regulatory risk. The CIO needs integration architecture, security boundaries, availability, scalability, operational ownership, technical dependencies, and lifecycle support.
Option A begins at an implementation depth unsuitable for the entire committee. Option B overweights the CFO's concerns and delays the rationale required by the other decision-makers. Option C ignores audience- specific responsibilities and forces every stakeholder to extract relevant information from an undifferentiated narrative.
The recommended structure is: decision and executive rationale, expected outcomes, accepted trade-offs, stakeholder-specific impact sections, major risks and controls, implementation implications, success metrics, and the requested governance decision. Technical appendices can preserve component-level detail without overwhelming the principal recommendation.
Study Guide references/topics: Executive communication; stakeholder differentiation; decision-first structure; financial, legal, and technology concerns; architectural recommendations.
NEW QUESTION # 44
You are defining escalation criteria for ambiguous cases in an automated workflow.
Which set of criteria best supports consistent escalation?
Answer: B
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 # 45
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