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.
EasyTechnical
0 practiced
You have a table model_predictions with columns: prediction_id, user_id, predicted_prob FLOAT, predicted_label INT (0/1), actual_label INT (0/1), event_date DATE. Write a SQL query (Postgres-compatible) that computes daily precision, recall, and F1 score for the last 30 days grouped by event_date. Handle NULL actual_label rows by excluding them and avoid division-by-zero.
HardTechnical
0 practiced
You need to measure the causal impact of a personalized pricing feature. Discuss identification strategies (randomized pricing experiments, regression discontinuity, instrumental variables, difference-in-differences), how you'd guard against confounding and selection bias, what metrics you'd measure (revenue, conversion, retention), and a rollout plan that balances business risk and statistical credibility.
HardSystem Design
0 practiced
Design a monitoring system that continuously tracks fairness metrics for a deployed classification model across protected attributes (e.g., race, gender, age). Specify which metrics (demographic parity, equalized odds, calibration within groups), statistical tests for detecting significant disparities, alerting thresholds, and automated/operational remediation strategies (e.g., reweighting, constrained retraining, human review).
HardTechnical
0 practiced
Describe practical techniques to quantify model uncertainty and how to surface that uncertainty in analytics dashboards for decision-making. Cover approaches such as probability calibration, ensembles to estimate variance, Bayesian approximations, prediction intervals for regression, and how you'd present uncertainty visually and in text for non-technical stakeholders.
EasyTechnical
0 practiced
As a data analyst, explain the practical differences between research ML work and production ML/AI work. Cover aspects such as objectives, reproducibility, testing, data requirements, performance vs. robustness trade-offs, deployment considerations, and how you would collaborate with research and engineering teams.
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