Analysis to Recommendation and Decision Framing Questions

Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.

MediumTechnical

0 practiced

You are evaluating whether to retrain a large production model on new data. Retraining is estimated to use 10,000 GPU-hours at $3/hour and is expected to yield a 3% relative lift in a revenue-driving metric. Provide a cost-benefit analysis that includes compute cost, expected incremental revenue (assume baseline monthly revenue $10M), time to implement, and a recommendation whether to retrain now or pursue cheaper alternatives. State assumptions and risks explicitly.

HardTechnical

0 practiced

Your executive sponsor asks you to justify a large upfront investment to build a proprietary large language model instead of continuing to use a vendor API. Prepare a recommendation memo outline that includes expected ROI (with sensitivity analysis), estimated timeline and milestones, technical risks, talent needs, competitive advantage argument, and a fallback plan if ROI targets are not met.

MediumSystem Design

0 practiced

Your product team asks whether to build a custom NER model or fine-tune a pre-trained LLM via transfer learning. Compare options on accuracy, development time, inference cost, maintenance, latency, data privacy, and vendor lock-in. Provide a clear recommendation for a mid-sized company with moderate traffic (200k requests/day), and include an estimated time-to-production and resource estimate for both options.

EasyTechnical

0 practiced

Two candidate classification models were evaluated offline: Model A F1=0.60, Model B F1=0.58. Model A inference cost is $0.02 per API call, Model B $0.015. Expected monthly calls: 1,000,000. Model A's training time is 200 GPU-hours, Model B 40 GPU-hours. As the AI Engineer deciding whether to deploy A or B, give a concise recommendation, quantify trade-offs (performance vs cost), show simple ROI calculation over 12 months, and state assumptions and limitations.

MediumTechnical

0 practiced

After a product change, customer churn increased by 0.8 percentage points in the exposed group. Describe a plan to test whether the change caused the churn increase: include the statistical methods you would use (for example difference-in-differences, regression, or re-randomization), instrumentation or data you need, and how you would decide whether to rollback the change conditionally.

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