AWS Certified Machine Learning – Specialty — Question 291

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.)

Answer options

• 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.

Correct answer: D, E

Explanation

For an ML-based predictive maintenance solution to be feasible, it requires a large volume of high-resolution historical data (Option D) and sufficient examples of the target failure modes to train a supervised model (Option E). If simple rule-based thresholds are already effective, an ML model is not necessary (Option B), while missing data (Option A) or a lack of failure examples across different crane models (Option C) would prevent successful model training.