Online Access Free Google.Professional-Machine-Learning-Engineer.v2022-02-28.q67 Practice Test (Page 5)

Professional-Machine-Learning-Engineer Exam Question 16

A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?

A.AWS DMS

B.Amazon Kinesis Data Streams

C.Amazon Kinesis Data Firehose

D.Amazon Kinesis Data Analytics

Professional-Machine-Learning-Engineer Exam Question 17

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

A.Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

B.Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

C.Write a direct connection to the SQL database within the notebook and pull data in

D.Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.

Professional-Machine-Learning-Engineer Exam Question 18

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A.Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.

B.Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

C.Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.

D.Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

Professional-Machine-Learning-Engineer Exam Question 19

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?

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.

Professional-Machine-Learning-Engineer Exam Question 20

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?

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

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Professional-Machine-Learning-Engineer Exam Question 16

Professional-Machine-Learning-Engineer Exam Question 17

Professional-Machine-Learning-Engineer Exam Question 18

Professional-Machine-Learning-Engineer Exam Question 19

Professional-Machine-Learning-Engineer Exam Question 20

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