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Amazon Web Services Updated MLA-C01 Exam Questions and Answers by gurfateh

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Amazon Web Services MLA-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning Engineer - Associate
Exam Code: MLA-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Associate
Questions: 241 Q&A's Shared By: gurfateh
Question 28

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model ' s algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

Options:

A.

Use AWS Glue to transform the categorical data into numerical data.

B.

Use AWS Glue to transform the numerical data into categorical data.

C.

Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

D.

Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Discussion
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Question 29

A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and distributed training.

An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.

What should the ML engineer do to meet the encryption requirement?

Options:

A.

Enable network isolation.

B.

Configure traffic encryption by using security groups.

C.

Enable inter-container traffic encryption.

D.

Enable VPC flow logs.

Discussion
Question 30

A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.

A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.

Which solution will meet these requirements with the LEAST implementation effort?

Options:

A.

Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.

B.

Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.

C.

Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.

D.

Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.

Discussion
Question 31

An ML engineer is building a logistic regression model to predict customer churn for subscription services. The dataset contains two string variables: location and job_seniority_level.

The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.

The ML engineer must perform preprocessing on the variables.

Which solution will meet this requirement?

Options:

A.

Apply tokenization to location. Apply ordinal encoding to job_seniority_level.

B.

Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level.

C.

Apply binning to location. Apply standard scaling to job_seniority_level.

D.

Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.

Discussion
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