Model Performance Analysis and Root Cause Analysis Questions
Techniques for diagnosing and troubleshooting production ML models, including monitoring metrics such as accuracy, precision, recall, ROC-AUC, latency and throughput; detecting data drift, feature drift, data quality issues, and model drift. Covers root-cause analysis across data, features, model behavior, and infrastructure, instrumentation and profiling, error analysis, ablation studies, and reproducibility. Includes remediation strategies to improve model reliability, performance, and governance in production systems.
EasyTechnical
0 practiced
Describe a simple approach to detect missing or malformed feature values arriving from an upstream ETL job in production. What statistics would you track, which thresholds would you alert on, and how would you prevent noisy false positives?
HardSystem Design
0 practiced
Design an automated root-cause analysis (RCA) pipeline that consumes model predictions, per-feature histograms, system traces, and business KPIs, and outputs a ranked list of probable causes with confidence scores. Describe the data schema, features for the RCA model/heuristics, candidate-generation strategies, ranking approach (heuristic vs supervised), and the human-in-the-loop review flow.
EasySystem Design
0 practiced
List and justify a minimum set of metrics and logs you would instrument for an online ML model to maintain observability. Include model-level metrics (e.g., prediction distributions), data-quality signals (e.g., null rates), and infrastructure metrics (e.g., p95 latency). For each item, describe how it helps detect common production issues.
HardTechnical
0 practiced
Devise a forensic and mitigation approach to detect and respond to model poisoning or training-data poisoning attacks in production. Include detection signals (e.g., sudden targeted performance drops, anomalous feature correlations), ingestion-time checks, quarantine strategies, rollback and retraining plans, and long-term prevention like robust training or provenance enforcement.
EasyTechnical
0 practiced
Given a confusion matrix from 10,000 predictions:TP = 90, FP = 10, FN = 910, TN = 8,990Compute accuracy, precision, recall, and F1. Interpret what these metrics indicate about model behavior and practical consequences in production.
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