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Amazon Web Services MLS-C01 Exam Dumps - Actual Questions Answers

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  • Last Update: Apr 19, 2025
  • Questions: 322 questions with Expert Explanation
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  • Multiple Choice: 54 Q&A's
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MLS-C01 Questions and Answers

Question # 1

A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.

Which steps must the data scientist take to improve model accuracy? (Select THREE.)

A.

Increase the amount of regularization that the model uses.

B.

Decrease the amount of regularization that the model uses.

C.

Increase the number of training examples that that model uses.

D.

Increase the number of test examples that the model uses.

E.

Increase the number of model features that the model uses.

F.

Decrease the number of model features that the model uses.

Question # 2

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A.

Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

B.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

C.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

D.

Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Question # 3

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.

Which approach should the ML specialist use to determine the ideal data transformations for the model?

A.

Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

B.

Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

C.

Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

D.

Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

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