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ML Systems & MLOps

Data Pipelines

5 questions

Multiple Choice

Data Pipeline Idempotency

Open-Ended

Data Validation

ETL vs ELT for ML

Data Versioning

Streaming vs Batch Processing

Training Infrastructure

5 questions

Multiple Choice

Distributed Training Strategy

Open-Ended

Experiment Tracking

ML Reproducibility

Distributed Training

GPU Resource Management

Model Serving

5 questions

Multiple Choice

Model Serving Latency Optimization

Open-Ended

Online vs Batch Inference

Model API Design

Model Packaging and Deployment

Scaling Inference Systems

Monitoring & Observability

5 questions

Multiple Choice

Detecting Model Drift in Production

Open-Ended

Data Quality Monitoring

ML Alerting Strategies

Model Drift

ML Performance Monitoring

CI/CD for ML

5 questions

Multiple Choice

ML-Specific CI/CD Considerations

Open-Ended

ML Deployment Strategies

Model Registry

Testing ML Systems

Model Rollback Strategies

Feature Engineering

5 questions

Multiple Choice

Feature Engineering for Time Series

Open-Ended

Feature Stores

Feature Reuse and Sharing

Feature Pipelines

Online Feature Serving