MLS-C01 Question #317: Real Exam Question with Answer & Explanation

Question

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.)

Options

• AThe historical sensor data does not include a significant number of data points and attributes for
• BThe historical sensor data shows that simple rule-based thresholds can predict crane failures.
• CThe historical sensor data contains failure data for only one type of crane model that is in
• DThe historical sensor data from the cranes are available with high granularity for the last 3 years.
• EThe historical sensor data contains most common types of crane failures that the company wants

Explanation

For an ML-based predictive maintenance solution to be suitable, there must be sufficient high-quality historical data, including diverse failure events, to allow the model to learn complex patterns.

Common mistakes.

• A. An insufficient number of data points and attributes directly hinders the ability of an ML model to learn robust patterns, making an ML solution unsuitable due to data scarcity.
• B. If simple rule-based thresholds are sufficient to predict failures, then an ML-based solution, which is more complex and resource-intensive, is not necessary or suitable, as simpler methods achieve the desired outcome.
• C. Historical failure data for only one type of crane model limits the generalizability of an ML model to the entire fleet, making it less suitable for a comprehensive predictive maintenance solution across diverse crane types.

Concept tested. ML problem suitability and data requirements

Reference. https://aws.amazon.com/machine-learning/ml-for-industrial-applications/predictive-maintenance/

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