A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

• A.Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
• B.Use a forecasting algorithm to run predictions.
• C.Use a regression algorithm to run predictions.
• D.Use a classification algorithm to run predictions
• E.Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
  "enrolled" or "not enrolled."

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A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social medi a. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?

• A.Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
• B.Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
• C.Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
• D.Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.

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A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

• A.Create a NAT gateway within the corporate VPC.
• B.Route Amazon SageMaker traffic through an on-premises network.
• C.Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
• D.Create VPC peering with Amazon VPC hosting Amazon SageMaker.

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A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?

• A.Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
• B.Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
• C.Write a direct connection to the SQL database within the notebook and pull data in
• D.Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in

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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 HPO job.
• B.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.
• C.Modify the HPO configuration as follows:
  
  Select the most accurate hyperparameter configuration form this training job.
• D.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.

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