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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q109-Q114):NEW QUESTION # 109
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?

A. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
B. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
C. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
D. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

Answer: B
Explanation:
The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data . References:
Amazon SageMaker Developer Guide: Train and Test Datasets
Amazon Personalize Developer Guide: Preparing and Importing Data

NEW QUESTION # 110
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?

A. The model predicts the trend well, but not the seasonality.
B. The model predicts the seasonality well, but not the trend.
C. The model predicts both the trend and the seasonality well.
D. The model does not predict the trend or the seasonality well.

Answer: D

NEW QUESTION # 111
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Answer: C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sage ... up-working-env.html

NEW QUESTION # 112
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

A. Bundle the NVIDIA drivers with the Docker image
B. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
C. Build the Docker container to be NVIDIA-Docker compatible
D. Organize the Docker container's file structure to execute on GPU instances.

Answer: A

NEW QUESTION # 113
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.
Perform a manual review on those words before performing a business validation.
B. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images.Use this information to process the loan applications.
C. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
D. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.

Answer: D
Explanation:
Explanation
The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.
Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.
Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.
Option D is incorrect because using Amazon Rekognition's feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.
References:
Amazon Augmented AI
Amazon SageMaker Ground Truth
Amazon Textract Operations
Amazon Rekognition

NEW QUESTION # 114
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