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Amazon AWS Certified Machine Learning - Specialty Exam Questions
AWS Certified Machine Learning - Specialty (MLS-C01) (Page 3 )

Updated On: 5-Mar-2026
Page 3 of 57
PDF with 370 Questions

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?

  1. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  2. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  3. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  4. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

Answer(s): C


Reference:

https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html



A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

  1. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
  2. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
  3. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
  4. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Answer(s): B


Reference:

https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html



A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

  1. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
  2. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
  3. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
  4. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Answer(s): B



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?

  1. 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.
  2. 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
  3. 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.
  4. 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. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Answer(s): A



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?

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

Answer(s): B



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