A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

• A.Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• B.Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
• C.Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• D.Load a smaller subset of the data into the SageMaker notebook and tram locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

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A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

• A.Use Amazon SageMaker Pipe mode.
• B.Use Amazon Machine Learning to train the models.
• C.Use Amazon Kinesis to stream the data to Amazon SageMaker.
• D.Use AWS Glue to transform the CSV dataset to the JSON format.

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A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

• A.Amazon QuickSight
• B.Amazon Redshift Spectrum
• C.AWS Glue
• D.Amazon Athena

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A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

• A.Modify the HPO configuration as follows:
  
  Select the most accurate hyperparameter configuration form this training job.
• B.Modify the HPO configuration as follows:
  
  Select the most accurate hyperparameter configuration form this HPO job.
• C.Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
  [0.01, 0.1]
  [0.001, 0.01]
  [0.0001, 0.001]
  Select the most accurate hyperparameter configuration form these three HPO jobs.
• D.Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
  [0.01, 0.1]
  [0.001, 0.01]
  [0.0001, 0.001]
  Select the most accurate hyperparameter configuration form these three HPO jobs.

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A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

• A.Use the event tracker in Amazon Personalize to include real-time user interactions.
• B.Add event type and event value fields to the interactions dataset in Amazon Personalize.
• C.Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
• D.Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

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