A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3 The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3 Which solution takes the LEAST effort to implement?

• A.Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
• B.Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
• C.Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
• D.Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

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A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

• A.Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
• B.Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
• C.Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
• D.Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

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A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A.Configure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
• B.Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
• C.Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
• D.Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

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A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?

• A.Drop the test samples with missing full review text fields, and then run through the test set.
• B.Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
• C.Use an algorithm that handles missing data better than decision trees.
• D.Generate synthetic data to fill in the fields that are missing data, and then run through the test set.

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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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