Business Context and Metrics Understanding Questions

Understand the broader business context for technical or operational work and identify relevant performance metrics. This includes recognizing the key performance indicators for different functions, translating technical outcomes into business impact, scoping a problem with success metrics and constraints, and using metrics to prioritize trade offs. Candidates should demonstrate how they would frame a problem in business terms before proposing technical or operational solutions.

EasyTechnical

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List common business KPIs you would consider when proposing an AI solution for different functions (product, sales, marketing, operations). For each function give two example KPIs and briefly explain why those KPIs matter for AI decisions and prioritization.

EasyTechnical

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Describe when you would prioritize precision over recall and vice versa in a production AI system. Provide two concrete business scenarios where each choice makes sense, and explain operational consequences (e.g., user experience, costs, manual review processes).

EasyTechnical

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Write a Python function that computes simple ROI and payback period for an AI feature that reduces customer-support calls. Inputs: baseline_calls_per_month (int), percent_reduction (float between 0 and 1), cost_per_call_usd (float), monthly_model_cost_usd (float). Return monthly_savings_usd, roi (savings/cost), months_to_payback (handle zero-division). Show sample usage with baseline=10000, reduction=0.10, cost_per_call=5.0, model_cost=2000.0.

MediumSystem Design

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You must deploy a real-time recommendation API with a 50ms 95th-percentile latency SLO, but larger models improve CTR significantly. As an AI Engineer, describe trade-offs and propose SLO definitions, techniques to meet latency while preserving model quality (e.g., distillation, caching, hybrid scoring), and how to communicate these trade-offs to stakeholders.

EasyTechnical

0 practiced

For a binary classification model that recommends premium subscriptions, accuracy increases from 85% to 88%, but conversion rate on the site does not change. As an AI Engineer, explain possible reasons for this discrepancy and list the diagnostic steps and additional metrics (e.g., calibration, threshold, class imbalance, cohort analysis) you would check.

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