Question 1

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.

How should the Specialist address this issue and what is the reason behind it?

Options :

A : The learning rate should be increased because the optimization process was trapped at a local minimum.

B : The dropout rate at the flatten layer should be increased because the model is not generalized enough.

C : The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

D : The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: B

Question 2

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

Options :

A : Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.

B : Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C : Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D : Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D

Question 3

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 4

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

Options :

A :

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique

Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create

a stream processor to detect faces from a collection of known employees, and alert when non-employees

are detected.

B :

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique

Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect

faces from a collection of known employees and alert when non-employees are detected.

C :

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to

Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and

create a stream processor to detect faces from a collection on each stream, and alert when nonemployees

are detected.

D :

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to

Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to

capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of

known employees, and alert when non-employees are detected.

Answer: A

Question 5

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.

Which techniques should the company use for feature selection? (Choose three.)

Options :

A : Data scaling with standardization and normalization

B : Correlation plot with heat maps

C : Data binning

D : Univariate selection

E : Feature importance with a tree-based classifier

F : Data augmentation

Answer: B,D,E