Question 1

A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns

Using this dataset for training, the Data Science team trained a random forest model that converged with over 99?curacy on the training set However, the prediction results on a test dataset were not satisfactory.

Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

Options :

A : Add more deep trees to the random forest to enable the model to learn more features.

B : indicate a copy of the samples in the test database in the training dataset

C : Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

D : Change the cost function so that false negatives have a higher impact on the cost value than false positives

E : Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: C,D

Question 2

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

Options :

A : Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B : Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C : Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

D : Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Answer: B

Question 3

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 4

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types. How should the company prepare the data for the model to improve the model's accuracy?

Options :

A : Adjust the class weight to account for each machine type

B : Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

C : Undersample the non-failure events. Stratify the non-failure events by machine type.

D : Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Answer: B

Question 5

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

Options :

A : The model predicts both the trend and the seasonality well.

B : The model predicts the trend well, but not the seasonality.

C : The model predicts the seasonality well, but not the trend.

D : The model does not predict the trend or the seasonality well.

Answer: D

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