Debugging and Troubleshooting AI Systems Questions

Covers systematic approaches to find and fix failures in machine learning and artificial intelligence systems. Topics include common failure modes such as poor data quality, incorrect preprocessing, label errors, data leakage, training instability, vanishing or exploding gradients, numerical precision issues, overfitting and underfitting, optimizer and hyperparameter problems, model capacity mismatch, implementation bugs, hardware and memory failures, and production environment issues. Skills and techniques include data validation and exploratory data analysis, unit tests and reproducible experiments, sanity checks and simplified models, gradient checks and plotting training dynamics, visualizing predictions and errors, ablation studies and feature importance analysis, logging and instrumentation, profiling for latency and memory, isolating components with canary or shadow deployments, rollback and mitigation strategies, monitoring for concept drift, and applying root cause analysis until the underlying cause is found. Interviewers assess the candidate on their debugging process, ability to isolate issues, use of tools and metrics for diagnosis, trade offs in fixes, and how they prevent similar failures in future iterations.