Question 1

A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR).

A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model.

Which solution will meet these requirements MOST cost-effectively?

Options :

A : Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances.

B : Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances.

C : Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances.

D : Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.

Answer: B

Question 2

A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.

Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

Options :

A : Apply integer transformation and set Red = 1, White = 5, and Green = 10.

B : Add new columns that store one-hot representation of colors.

C : Replace the color name string by its length.

D : Create three columns to encode the color in RGB format.

E : Replace each color name by its training set frequency.

Answer: B,D

Question 3

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

Options :

A : Seq2seq

B : XGBoost

C : K-means

D : Random Cut Forest (RCF)

Answer: B

Question 4

A healthcare company is using an Amazon SageMaker notebook instance to develop machine learning (ML) models. The company's data scientists will need to be able to access datasets stored in Amazon S3 to train the models. Due to regulatory requirements, access to the data from instances and services used for training must not be transmitted over the internet. Which combination of steps should an ML specialist take to provide this access? (Choose two.)

Options :

A : Configure the SageMaker notebook instance to be launched with a VPC attached and internet access disabled.

B : Create and configure a VPN tunnel between SageMaker and Amazon S3

C : Create and configure an S3 VPC endpoint Attach it to the VPC.

D : Create an S3 bucket policy that allows traffic from the VPC and denies traffic from the internet.

E : Deploy AWS Transit Gateway Attach the S3 bucket and the SageMaker instance to the gateway.

Answer: A,C

Question 5

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

Options :

A : Emails exchanged by customers and the company-s customer service agents

B : Social media posts containing the name of the company or its products

C : A publicly available collection of news articles

D : A publicly available collection of customer reviews

E : Product sales revenue figures for the company

F : Instruction manuals for the company-s products

Answer: A,B,D

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