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
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.
HardTechnical
0 practiced
You must define SLAs/SLOs for a prediction API balancing latency (p95 < 200ms), availability (99.9%), and model accuracy (precision >= 0.85 on a critical class). Explain how you would set error budgets, alerting tiers, automated throttling/fallback strategies, and communication/compensation policies in case of breaches.
EasyTechnical
0 practiced
What is model calibration and why is it important for production systems that use predicted probabilities (for example, risk scoring or pricing)? Describe how to evaluate calibration (reliability diagram/calibration plot, Expected Calibration Error, Brier score) and name one method to improve calibration in deployed models.
MediumTechnical
0 practiced
Describe an ablation study plan to identify which set of features likely caused a recent degradation in production performance. Include experiment design (which groups of features to ablate and the order), statistical controls, how to track variance, and how to interpret ambiguous/noisy results.
HardSystem Design
0 practiced
Design a reproducible ML artifact storage and provenance system ensuring any production prediction can be traced to the exact training dataset snapshot, feature-processing code, model binary, and runtime environment. Outline data model (artifacts and metadata), storage choices, lookup APIs, and retention/compliance considerations for regulated data.
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