A company operates large cranes at a busy port The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity. The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and temperature for each crane. The company contracts AWS ML experts to implement an ML solution. Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Choose two.)

Topic #: - All AWS Certified Machine Learning - Specialty Questions

A. The historical sensor data does not include a significant number of data points and attributes for certain time periods.
B. The historical sensor data shows that simple rule-based thresholds can predict crane failures.
C. The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.
D. The historical sensor data from the cranes are available with high granularity for the last 3 years.
E. The historical sensor data contains most common types of crane failures that the company wants to predict.

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