A port operator collects real-time sensor data from large cranes (rotation speed, tension, energy, vibration, pressure, temperature) and wants to apply ML for predictive maintenance. Which findings would indicate that an ML-based predictive maintenance solution is appropriate? (Choose two.)

For an ML-based predictive maintenance solution to be effective, it requires sufficient and relevant historical data. "Sensor data are available at high granularity for the past 3 years" is correct because high-granularity data over a significant period provides the rich feature set and historical context necessary for ML models to learn patterns preceding failures. "The historical records include most common types of crane failures that the company intends to predict" is also correct because an ML model needs examples of the target outcomes (failures) to learn how to predict them. Without diverse failure examples, the model cannot generalize effectively. Conversely, missing data points or attributes (first incorrect option) would hinder model training. If simple threshold rules already work reliably (second incorrect option), an ML solution might be overkill or offer marginal improvement. Lacking failure examples for most crane types (third incorrect option) would severely limit the model's applicability and accuracy across the fleet.