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 the EC2 instances within the customer account, but they run outside of VPCs.
• 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 AWS ECS instances running within AWS service accounts.

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A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?

• A.Amazon Kinesis Data Analytics
• B.Amazon Kinesis Data Firehose
• C.AWS DMS
• D.Amazon Kinesis Data Streams

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A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

• A.Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
• B.Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
• C.Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
• D.A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

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A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

• A.The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
• B.The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
• C.The precision of the model is 86%, which is greater than the accuracy of the model.
• D.The precision of the model is 86%, which is less than the accuracy of the model.

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A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

• A.RecordIO
• B.Compressed JSON
• C.CSV files
• D.Parquet files

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