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

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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: chaim
Question 16

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?

Options:

A.

Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.

B.

Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

C.

Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.

D.

Run a SageMaker training job with an EFS file system as the data source.

Discussion
Question 17

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

Options:

A.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B.

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D.

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Discussion
Question 18

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.

What changes should the Specialist consider to solve this issue? (Choose three.)

Options:

A.

Choose a higher number of layers

B.

Choose a lower number of layers

C.

Choose a smaller learning rate

D.

Enable dropout

E.

Include all the images from the test set in the training set

F.

Enable early stopping

Discussion
Question 19

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

Options:

A.

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

B.

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

C.

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

D.

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Discussion
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