Amazon Exam MLS-C01 Topic 3 Question 96 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 96
Topic #: 3

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 ...perature 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? (Select TWO.)

AThe historical sensor data does not include a significant number of data points and attributes for certain time periods.

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 operation and lacks failure data of most other types of crane that are in operation.

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 to predict.

Show Suggested Answer

Suggested Answer: A

Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability. Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.

The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

References:

* Stratified Sampling for Imbalanced Datasets

* Imbalanced Data

* Tour of Data Sampling Methods for Imbalanced Classification

by Geraldine at Jul 04, 2024, 03:10 AM

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Nathan

8 hours ago

I agree with you, Carin. If simple rules can predict failures, then ML can definitely improve on that.

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Carin

3 days ago

I think option B is a good indicator for using ML.

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