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
0 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
0 practiced
Implement a Python function (standard library or numpy/pandas allowed) that takes arrays y_true and y_pred (binary 0/1) and returns a dict with accuracy, precision, recall, and F1. Handle edge cases (no positive predictions or no positive labels) and document behavior when metrics are undefined.
MediumTechnical
0 practiced
You observe that precision for a fraud model has been decreasing while recall remains stable. As a data analyst, describe the step-by-step diagnostics you would run (including SQL queries, visualizations, and cohorts), likely root causes (e.g., threshold shift, class imbalance, operational changes), and remediation options including threshold tuning and retraining strategies.
MediumTechnical
0 practiced
Case study: a targeting model used for promotional emails was rolled out and unsubscribe rates increased by 30% relative to baseline. As the data analyst, design an analysis plan to determine whether the model or other campaign changes caused the spike. Include data joins, segmentation, uplift analysis, possible confounders, and immediate mitigations you would recommend.
EasyBehavioral
0 practiced
Behavioral: Describe a concrete project where you collaborated with data scientists or ML engineers to prepare data for a model destined for production. Use the STAR format: situation, your tasks (data cleaning, feature computation, validation), actions (SQL logic, checks, pipelines), results (metrics, business impact), and what you learned.
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