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 need to ingest gzipped JSON log files dropped into S3 into a data lake daily. Outline a simple, reliable batch ingestion pipeline a data engineer would implement: include components (discovery, validation, transformation, storage), scheduling, error handling, idempotency, and how to handle late-arriving or duplicate files. Describe what metadata you would store to make ingest jobs auditable and resumable.
HardTechnical
0 practiced
You inherited an ML data platform with runaway cloud spend due to long-running training jobs, stale feature store partitions, and over-retained experiment artifacts. Propose a phased cost-reduction plan with quick wins and longer-term changes: include budgeting and chargeback, use of spot/preemptible instances, lifecycle policies for artifacts, autoscaling, and governance controls to prevent future budget overruns. Prioritize actions and justify ROI.
HardSystem Design
0 practiced
Design a globally distributed model serving system that reduces inference latency for users worldwide and supports frequent model updates with near-zero downtime. Discuss edge vs regional serving, model caching strategies, consistency of features across regions, handling models with stateful preprocessors, and safe rollback mechanisms for corrupted deployments.
HardSystem Design
0 practiced
Design a multi-region ML data pipeline where raw PII must remain in the user's home region but aggregated or anonymized models can be globally accessed. Explain data partitioning, federated training vs central training with differential privacy, feature aggregation across regions, and how to safely distribute global model artifacts while satisfying data residency and latency requirements.
EasyTechnical
0 practiced
Describe common evaluation metrics for classification and regression models that a data engineer should know (for example: precision, recall, F1, ROC-AUC, accuracy for classification; MAE, MSE, RMSE for regression). For each metric, briefly state when it matters for operational monitoring and how a data engineer would compute and store these metrics periodically in production for dashboarding and alerting.
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