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MLS-C01 Questions and Answers

Question # 1

A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days

Which of the following modeling techniques should the Specialist use1?

A.

Time-series prediction

B.

Anomaly detection

C.

Binary classification

D.

Regression

Question # 2

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models

During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images

Which of the following should be used to resolve this issue? (Select TWO)

A.

Add vanishing gradient to the model

B.

Perform data augmentation on the training data

C.

Make the neural network architecture complex.

D.

Use gradient checking in the model

E.

Add L2 regularization to the model

Question # 3

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.

Which metrics should the data scientist use to optimize the model? (Choose two.)

A.

Specificity

B.

False positive rate

C.

Accuracy

D.

Area under the precision-recall curve

E.

True positive rate