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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by arwen

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 281 Q&A's Shared By: arwen
Question 20

A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns

Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.

Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

Options:

A.

Add more deep trees to the random forest to enable the model to learn more features.

B.

indicate a copy of the samples in the test database in the training dataset

C.

Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

D.

Change the cost function so that false negatives have a higher impact on the cost value than false positives

E.

Change the cost function so that false positives have a higher impact on the cost value than false negatives

Discussion
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Question 21

A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.

What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

Options:

A.

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.

B.

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-iearn multi-dimensional scaling (MDS) algorithm.

C.

Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.

D.

Use the SageMaker k-means algorithm with k of less than 1.000 to train the model

E.

Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.

Discussion
Question 22

A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:

Questions 22

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.

Which solution provides the MOST accurate result?

Options:

A.

Modify the HPO configuration as follows:

Option A 22

Select the most accurate hyperparameter configuration form this HPO job.

B.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:

[0.01, 0.1]

[0.001, 0.01]

[0.0001, 0.001]

Select the most accurate hyperparameter configuration form these three HPO jobs.

C.

Modify the HPO configuration as follows:

Option C 22

Select the most accurate hyperparameter configuration form this training job.

D.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:

[0.01, 0.1]

[0.001, 0.01]

[0.0001, 0.001]

Select the most accurate hyperparameter configuration form these three HPO jobs.

Discussion
Question 23

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.

Which implementation will meet these requirements?

Options:

A.

Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

B.

Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.

C.

Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

D.

Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

Discussion
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