AWS Certified Machine Learning – Specialty Exam (MLS-C01) - AWS Actual Exam Questions
Last updated on April 11, 2026
[Data Engineering] A data science team is working with a tabular dataset that the team stores in Amazon S3. The team wants to experiment with different feature transformations such as categorical feature encoding. Then the team wants to visualize the resulting distribution of the dataset. After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations. Which solution will meet these requirements with the MOST operational efficiency?
Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.
Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualzation. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.
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[Data Engineering] A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements: * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. * Support event-driven ETL pipelines. * Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?
Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
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[Modeling] 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.)
Increase the amount of regularization that the model uses.
Decrease the amount of regularization that the model uses.
Increase the number of training examples that that model uses.
Increase the number of test examples that the model uses.
Increase the number of model features that the model uses.
Decrease the number of model features that the model uses.
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[Data Engineering] A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10.000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels * 224 pixels. After several training runs, the model is overfitting on the training data. Which actions should the ML specialist take to address this problem? (Select TWO.)
Use Amazon SageMaker Ground Truth to label the unlabeled images
Use image preprocessing to transform the images into grayscale images.
Use data augmentation to rotate and translate the labeled images.
Replace the activation of the last layer with a sigmoid.
Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.
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[Modeling] A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color. Which solution will meet these requirements with the LEAST amount of effort?
Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
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