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MLS-C01 Dumps With Exact Questions and Answers

Exact2pass Provides 100% Valid AWS Certified Machine Learning - Specialty MLS-C01 Exam dumps Questions and answers which can helps you to Pass Your Certification Exam in First Attempt.

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MLS-C01 Questions and Answers

Question # 1

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?


Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.


Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.


Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.


Run a SageMaker training job with an EFS file system as the data source.

Question # 2

A manufacturer is operating a large number of factories with a complex supply chain relationship where

unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to

analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then

dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can

include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.

To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though

many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to

maintain near-real-time inference capabilities.

Which deployment architecture for the model will address these business requirements?


Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines

need maintenance.


Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer

which machines need maintenance.


Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch

report to identify machines that need maintenance.


Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table.

Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.

Question # 3

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target value?


Root Mean Square Error (RMSE)


Residual plots


Area under the curve


Confusion matrix

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