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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions |
| Topic 2: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs - Document architectures and support full lifecycle phases |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Define evaluation metrics and success criteria - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Topic 7: Integration | 19% | - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations |
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NEW QUESTION # 104
A customer support team has proposed delegating customer refund decisions to a Claude-driven workflow with no human review for refunds under 50 USD. The team's reasoning is that small refunds are low-risk and human review would erase the efficiency gain.
Which Delegation-competency principle should guide your response?
Answer: B
Explanation:
Transaction value is only one component of risk. Even a small refund can create fraud exposure, discriminatory outcomes, contractual violations, account compromise, policy inconsistency, or cumulative financial loss. Delegation should therefore depend on the nature, detectability, reversibility, and aggregate impact of failure-not a monetary threshold alone. Option B supports risk-stratified automation: routine, well- evidenced cases may proceed automatically, while anomalous, identity-sensitive, disputed, or policy-edge cases require review or escalation. Anthropic's trustworthy-agent principles emphasize maintaining meaningful human control rather than imposing universal review or unrestricted autonomy. Option A is unnecessarily absolute; Option C prioritizes efficiency over governance; and Option D prohibits potentially safe, controlled automation without risk analysis.
Study Guide references/topics: Delegation competency; human control; risk classification; reversibility; cumulative exposure; escalation design.
NEW QUESTION # 105
You are evaluating an evaluation set used to score a Claude-based hiring-support tool. The set is drawn from one geographic region and one tenure band.
Which response is most appropriate?
Answer: D
Explanation:
The existing evaluation set does not represent the population or operating conditions the hiring-support system will encounter. Option B corrects this coverage defect by expanding geographic and tenure slices and requiring the system to be rescored before broader deployment.
Aggregate performance on one narrow group can conceal substantial differences among regions, experience levels, job families, languages, and other relevant cohorts. The expanded dataset should therefore support disaggregated metrics, not merely one combined score. It should also include representative, edge, and adversarial cases, with labels and grading procedures reviewed for consistency and potential bias.
Anthropic's evaluation guidance states that evaluations should mirror the real-world task distribution and include edge cases. Success criteria should also be relevant to the application's actual purpose and users.
Define Success Criteria and Build Evaluations
Option A replaces one unrepresentative method with another. Option C deliberately narrows coverage further.
Option D mistakes high performance on a restricted subset for evidence of generalization.
Because hiring is a consequential domain, the organization should combine representative quantitative evaluation, subgroup analysis, expert review, monitoring, access controls, transparency, and meaningful human responsibility for final decisions.
Study Guide references/topics: Dataset representativeness; evaluation slices; subgroup performance; bias detection; consequential-use governance; pre-release rescoring.
NEW QUESTION # 106
You are integrating Claude Code into the team's pull-request workflow. The team wants AI-assisted review without removing human approval.
Which integration design best fits this requirement?
Answer: A
Explanation:
Option A augments the existing review process without transferring final accountability to the model. Claude Code can inspect the change in repository context, identify logic defects, security weaknesses, regressions, and maintainability concerns, and publish structured findings for human evaluation. Existing branch protection then preserves required approvals and separation of duties. Automatic merging or disabling protection rules would convert an advisory capability into an uncontrolled authorization mechanism. Silent deletion would eliminate transparency and prevent authors from challenging or correcting the assessment.
Anthropic's Code Review documentation states that findings are posted as comments and do not approve or block the pull request, leaving the established review workflow intact. Claude Code Review
NEW QUESTION # 107
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?
Answer: A
Explanation:
Option C addresses both components responsible for the excessive cost. Anthropic prompt caching allows stable, repeatedly submitted prompt material-such as system instructions, tool definitions, and reusable background information-to be placed in a consistent prefix. After that prefix is written to the cache, qualifying subsequent requests can reuse it at the lower cache-read cost instead of repeatedly processing the same content at the standard input-token rate.
Retrieval must be optimized separately. Supplying an entire document collection or excessively deep search results increases cost, consumes context capacity, and may reduce answer quality by surrounding the relevant evidence with distracting material. Retrieval should select the smallest set of authoritative passages that provides sufficient evidence for the current query. This typically requires relevance scoring, deduplication, metadata filtering, reranking, and explicit token-budget limits.
Increasing retrieval depth or expanding the repeated system prompt directly worsens the identified cost driver.
Moving every request to a heavier model changes the unit economics but does not correct inefficient context construction. The recommended optimization therefore combines prefix caching with query-specific context pruning, followed by evaluation to confirm that reduced context does not lower task accuracy.
Study Guide references/topics: [Prompt caching](https://docs.anthropic.com/en/docs/build-with-claude
/prompt-caching); [effective context engineering] (https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents); retrieval precision; token-cost analysis; cache-prefix stability.
NEW QUESTION # 108
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 # 109
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