AI and Machine Learning Background Questions
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
HardSystem Design
82 practiced
Design an offline-to-online validation pipeline for model updates before deployment. Include shadow testing or mirrored traffic, canary rollout design, metrics to compare (prediction agreement, calibration, business KPIs), automated acceptance criteria, monitoring windows, and rollback strategies. Describe data instrumentation and logging needed for safe comparisons.
EasyTechnical
88 practiced
You're a data analyst explaining ML fundamentals to product stakeholders. Describe the differences between supervised, unsupervised, and reinforcement learning. For each: give a concise definition, one real-world business example (product analytics, marketing or ops), the typical inputs/outputs, and when a data analyst would prefer that approach.
MediumTechnical
77 practiced
Write PostgreSQL SQL to compute Population Stability Index (PSI) between training_predictions(user_id, predicted_prob) and current_predictions(user_id, predicted_prob). Use 10 equal-width bins of predicted_prob [0,1). Show per-bin training_pct, current_pct, and PSI contribution, and the final PSI sum. Handle zero counts by adding a tiny epsilon to avoid division by zero.
HardSystem Design
80 practiced
Design an end-to-end inference architecture for real-time personalization at scale: target throughput 100k requests/sec and p95 latency <100ms. Describe data ingestion, feature retrieval (online store vs cache), model serving (serverless vs dedicated), caching layers, monitoring and logging requirements, fallback behavior, and cost/operational trade-offs. As a data analyst, specify what telemetry you would require to validate model behavior in production.
EasyTechnical
65 practiced
Define precision, recall, F1, AUC-ROC and AUC-PR at a level suitable for business stakeholders. For a fraud-detection model vs. a marketing-conversion model, explain which metric(s) you would prioritize and why, and give one numeric example that illustrates the precision/recall trade-off.
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