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Last Updated: Aug 25, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Evaluation, Testing & Optimization | 16% | - Production monitoring and optimization - Evaluation framework design - Evaluation metrics and datasets - Cost and performance optimization - A/B testing - System issue diagnosis |
| Governance, Safety & Risk Management | 14% | - Security and risk management - AI safety and guardrails - Ethical AI considerations - Human-in-the-loop validation - Regulatory and compliance requirements |
| Solution Design & Architecture | 17% | - Translating business problems into Claude-based AI solutions - Architectural patterns
- Alignment with business value, cost, performance, and SLAs - Multi-agent systems and orchestration - Decomposition techniques for complex problem solving |
| Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Context window optimization - System prompts and prompt templates - Guardrails - Claude model selection and trade-offs |
| Integration | 19% | - Claude integration mechanisms
- Enterprise system integration |
| Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Debugging and operational issue resolution - Claude tooling configuration for teams - Developer enablement |
| Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Solution lifecycle management - Architecture documentation - Stakeholder management - Communicating architectural decisions - Service-level agreements |
1. A Claude Architect is reviewing a post-deployment performance report for an AI-assisted legal-document summarization system. The report includes these observations:
* Average summarization time decreased from 47 minutes to 6 minutes per document.
* Associates spend less time on summaries, but overall billable output has not measurably changed.
* Infrastructure costs increased by 22% because redundant retry logic generated additional API calls.
* Some summaries require attorney correction, adding an average of 8 minutes of review per document.
Which analysis correctly attributes each observation to the appropriate business-value pillar?
A) Observation 1 indicates an efficiency gain; Observation 2 shows that productivity has not yet been realized; Observation 3 is a solution-cost issue; Observation 4 is an efficiency loss that partially offsets Observation 1.
B) Observations 1 and 2 both indicate efficiency gains; Observation 3 is a solution-cost issue; Observation
4 is a performance-SLA issue.
C) Observation 1 is a transformation outcome; Observation 2 is an efficiency gain; Observation 3 is a performance-SLA degradation; Observation 4 is a solution-cost issue.
D) Observations 1 and 4 together indicate a net performance-SLA improvement; Observation 2 is a transformation gap; Observation 3 is a productivity drain from over-engineering.
2. You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
A) Increase retrieval depth on every request to maximize recall, worsening the dominant cost driver by adding more retrieved tokens per request rather than reducing them.
B) Move the long, repeated system prompt into a cacheable prefix and trim retrieved context to the spans relevant to each query.
C) Add additional repeated content to the system prompt to give the model more guidance.
D) Switch every request to the heaviest available model to maximize output quality, accepting that higher per-request inference cost compounds rather than addresses the dominant cost driver.
3. You are operating an interactive assistant whose dominant performance constraint is per-turn latency. Quality on routine turns is already acceptable.
Which configuration adjustment most directly improves latency without disproportionately damaging quality?
A) Reduce retrieval depth to the top-k passages that historically cover the answer, and cache stable system- prompt content.
B) Disable prompt caching entirely to ensure fresh context processing on every request, preventing stale prefix content from affecting latency-sensitive interactions.
C) Increase retrieval depth to the corpus maximum to improve recall regardless of latency.
D) Switch every turn to the heaviest available model to maximize output quality, accepting that the increased model latency will worsen the per-turn SLO rather than improve it.
4. 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.
A) Identify the decision points in the pipeline that require human oversight by impact and reversibility.
B) Iterate the workflow design based on observed pilot findings before broader rollout to production.
C) Document the workflow with check criteria, escalation paths, and service-level agreements (SLAs) for each step.
D) Define the sampling strategy and the escalation criteria at each oversight point in the pipeline.
E) Onboard the reviewer pool with role-specific training on the check criteria and escalation procedures.
5. You are explaining the precedence of Claude Code configuration scopes to the team.
Which precedence ordering, from highest to lowest, is correct?
A) Managed # command-line arguments # local # project # user
B) User # project # local # command-line arguments # managed
C) Project # user # managed # local # command-line arguments
D) Local # managed # user # command-line arguments # project
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: A,D | Question # 5 Answer: A |
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