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

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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: 307 Q&A's Shared By: theia
Question 68

A Data Engineer needs to build a model using a dataset containing customer credit card information.

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Options:

A.

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker

instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically

discard credit card numbers and insert fake credit card numbers.

C.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker

instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length

of the credit card numbers.

D.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Discussion
Question 69

A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.

Which strategy will allow the data scientist to identify fraudulent accounts?

Options:

A.

Execute the built-in FindDuplicates Amazon Athena query.

B.

Create a FindMatches machine learning transform in AWS Glue.

C.

Create an AWS Glue crawler to infer duplicate accounts in the source data.

D.

Search for duplicate accounts in the AWS Glue Data Catalog.

Discussion
Question 70

An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.

Which data transformation step should the data scientist take to improve the predictions of the model?

Options:

A.

One-hot encoding

B.

Cartesian product transformation

C.

Quantile binning

D.

Normalization

Discussion
Question 71

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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