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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Alignment with business value, cost, performance, and SLAs - Multi-agent systems and orchestration - Architectural patterns
- End-to-end architecture design - Decomposition techniques for complex problem solving |
| Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - A/B testing - Production monitoring and optimization - Evaluation framework design - Cost and performance optimization - System issue diagnosis |
| Governance, Safety & Risk Management | 14% | - Security and risk management - Human-in-the-loop validation - Regulatory and compliance requirements - AI safety and guardrails - Ethical AI considerations |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Stakeholder management - Service-level agreements - Discovery and requirements gathering - Architecture documentation - Solution lifecycle management |
| Integration | 19% | - Authentication and authorization analysis - RAG pipeline design
|
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams |
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Context window optimization - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Guardrails |
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NEW QUESTION # 75
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 # 76
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.
Answer: B,E
NEW QUESTION # 77
You are supporting an EU-based deployment with GDPR obligations.
Which combination of measures best supports the deployment's GDPR posture?
Answer: A
Explanation:
Option A combines the contractual, technical, and operational measures relevant to a defensible GDPR posture. For commercial services, the Data Processing Addendum defines processor obligations and incorporates safeguards such as Standard Contractual Clauses where applicable. Anthropic states that its DPA is incorporated into its Commercial Terms, while the customer remains responsible for determining lawful purposes, issuing processing instructions, and fulfilling controller obligations.
A defined retention configuration ensures personal data is not retained indefinitely without a documented purpose. Redacting personal information that is unnecessary for the task implements data minimization and reduces exposure in prompts, logs, retrieval stores, and model outputs. Documented data-subject-rights procedures support access, correction, deletion, restriction, and other applicable requests by ensuring the organization can locate and act on relevant records.
Enterprise deployment alone does not establish compliance; the organization must configure and govern the service consistently with its processing activities, risk assessment, lawful basis, and contractual commitments.
Options B, C, and D intentionally remove those controls or promote excessive collection.
Study Guide references/topics: [Anthropic Data Processing Addendum guidance](https://privacy.anthropic.
com/en/articles/7996862-how-do-i-view-and-sign-your-data-processing-addendum-dpa); [commercial data- retention practices] (https://privacy.anthropic.com/en/articles/7996866-how-long-do-you-store-my- organization-s-data); GDPR data minimization; retention governance; data-subject-rights procedures.
NEW QUESTION # 78
You are integrating human review into a high-volume classification pipeline where reviewing every output is infeasible.
Which sampling strategy best balances throughput with quality oversight?
Answer: D
Explanation:
Risk-stratified sampling concentrates limited reviewer capacity where error consequences and uncertainty are greatest. All low-confidence and high-impact cases should receive mandatory review, while random sampling of routine high-confidence outputs provides an unbiased signal for drift, unexpected failure modes, and overconfident errors. Complaint-only monitoring discovers defects after harm occurs. Reviewing only high- confidence cases systematically ignores the most dangerous outputs. Universal review provides maximum coverage but contradicts the stated throughput constraint and may create queues that undermine service objectives. The sampling policy should define confidence calibration, impact categories, escalation thresholds, review SLAs, and periodic adjustment based on observed error rates. Reviewer decisions should feed the evaluation dataset so monitoring quality improves over time.
NEW QUESTION # 79
You are comparing two configurations for an internal compliance Q & A assistant. Configuration X reduces top-k retrieval and uses a lighter model, lowering p95 latency by 38 percent while producing a measured 2 percent reduction in the accuracy benchmark. Configuration Y retains the existing top-k retrieval setting and heavier model, with unchanged p95 latency and no measurable change in accuracy. The deployment's stated priorities are accuracy first and latency second.
Which configuration is the better recommendation?
Answer: B
Explanation:
Configuration Y is the appropriate recommendation because architectural optimization must follow the deployment's explicitly defined priorities and acceptance criteria. Accuracy is the primary requirement for this compliance assistant, and Configuration X introduces a measured two-percent accuracy regression. That reduction cannot be dismissed as ordinary benchmark variation unless repeated evaluation establishes that it is statistically insignificant. In a compliance workload, even a small accuracy decline can increase incorrect guidance, missed policy requirements, human-review effort, and regulatory exposure. Configuration Y preserves the established accuracy level while maintaining the current latency profile. Although its latency does not improve, latency is explicitly the secondary objective. Configuration X could be reconsidered only if further testing eliminates the accuracy regression or stakeholders formally approve a revised trade-off.
Options C and D incorrectly impose a general preference for latency over the documented deployment priorities.
NEW QUESTION # 80
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