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Question 81

A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data.
The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience.
Which solution will meet these requirements with the LEAST amount of effort from the internal team?

A.Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
B.Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
C.Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
D.Set up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

Correct Answer: A

The solution A will meet the requirements with the least amount of effort from the internal team because it uses Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:
Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Amazon SageMaker Ground Truth is a service that can create high-quality training datasets for machine learning by using human labelers. A private workforce is a group of labelers that the company can manage and control. The internal team can use the private workforce to label the satellite images as having solar panels or not. The SageMaker Ground Truth active learning feature can reduce the labeling effort by using a machine learning model to automatically label the easy examples and only send the difficult ones to the human labelers1.
Use Amazon Rekognition Custom Labels for model training and hosting. Amazon Rekognition Custom Labels is a service that can train and deploy custom machine learning models for image analysis. Amazon Rekognition Custom Labels can use the labeled data from SageMaker Ground Truth to train a model that can detect solar panels in satellite images. Amazon Rekognition Custom Labels can also host the model and provide an API endpoint for inference2.
The other options are not suitable because:
Option B: Setting up a private workforce that consists of the internal team, using the private workforce to label the data, and using Amazon Rekognition Custom Labels for model training and hosting will incur more effort from the internal team than using SageMaker Ground Truth active learning feature. The internal team will have to label all the images manually, without the assistance of the machine learning model that can automate some of the labeling tasks1.
Option C: Setting up a private workforce that consists of the internal team, using the private workforce and the SageMaker Ground Truth active learning feature to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead than using Amazon Rekognition Custom Labels. The company will have to manage the SageMaker training job, the model artifact, and the batch transform job. Moreover, SageMaker batch transform is not suitable for real-time inference, as it processes the data in batches and stores the results in Amazon S33.
Option D: Setting up a public workforce, using the public workforce to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead and cost than using a private workforce and Amazon Rekognition Custom Labels. A public workforce is a group of labelers from Amazon Mechanical Turk, a crowdsourcing marketplace. The company will have to pay the public workforce for each labeling task, and it may not have full control over the quality and security of the labeled data. The company will also have to manage the SageMaker training job, the model artifact, and the batch transform job, as explained in option C4.
References:
1: Amazon SageMaker Ground Truth
2: Amazon Rekognition Custom Labels
3: Amazon SageMaker Object Detection
4: Amazon Mechanical Turk

Question 82

Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.
Which solution will meet these requirements with the LEAST development time?

A.Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
B.Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
C.Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
D.Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Question 83

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A.The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20
85%
B.The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
1312%
C.The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 -
0.32).
D.The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Question 84

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% accuracy 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.)

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

Question 85

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

A.Principal component analysis (PCA)
B.Linear regression
C.K-means
D.Logistic regression