Free MLS-C01 Braindumps


  • Exam Number: MLS-C01
  • Provider: Amazon
  • Questions: 106
  • Updated On: 26-Sep-2020



QUESTION: 1
A web-based company wants to improve its conversion rate on its landing page Using a large
historical dataset of customer visits, the company has repeatedly trained a multi-class deep
learning network algorithm on Amazon SageMaker However there is an overfitting problem
training data shows 90% accuracy in predictions, while test data shows 70% accuracy only

The company needs to boost the generalization of its model before deploying it into production
to maximize conversions of visits to purchases.

Which action is recommended to provide the HIGHEST accuracy model for the company's test
and validation data?

A. Increase the randomization of training data in the mini-batches used in training.
B. Al ocate a higher proportion of the overall data to the training dataset
C. Apply L1 or L2 regularization and dropouts to the training.
D. Reduce the number of layers and units (or neurons) from the deep learning network.

Answer(s): A
QUESTION: 2
A Data Scientist is developing a machine learning model to classify whether a financial
transaction is fraudulent. The labeled data available for training consists of 100,000 non-
fraudulent observations and 1,000 fraudulent observations.

The Data Scientist applies the XGBoost algorithm to the data, resulting in the following
confusion matrix when the trained model is applied to a previously unseen validation dataset.
The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the
number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false
positive predictions by the model? (Select TWO.)

A. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and
negative weights.
C. Increase the XGBoost max_depth parameter because the model is currently underfitting the
data.
D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the
data.

Answer(s): D, E
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