MLS-C01 Exam Dumps

Some new quesstions:
Q
An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection – TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.
Which approach should the ML specialist use to improve the performance of the model on the testing data?
A. Increase the value of the momentum hyperparameter.
B. Reduce the value of the dropout_rate hyperparameter.
C. Reduce the value of the learning_rate hyperparameter.
D. Increase the value of the L2 hyperparameter.

Q
A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company’s stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?
A. Aggregate sales from stores in the same geographic area.
B. Apply smoothing to correct for seasonal variation.
C. Change the forecast frequency from daily to weekly.
D. Replace missing values in the dataset by using linear interpolation.

Q
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.
After training, the model’s inferences accuracy is lower than expected.
Which preprocessing technique will result in the GREATEST increase of the model’s inference accuracy?
A. Normalize the problematic features.
B. Bootstrap the problematic features.
C. Remove the problematic features.
D. Extrapolate synthetic features.

Q
A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model’s accuracy?
A. Adjust the class weight to account for each machine type.
B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
C. Undersample the non-failure events. Stratify the non-failure events by machine type.
D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

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