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AWS Certified Machine Learning Specialty MLS-C01 Exam (MLS-C01) - AWS Exam Questions
Last updated on July 22, 2026
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Question
[Modeling] A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?
Which feature engineering technique should the Data Scientist use to meet the objectives?
Options
A
Use an autoencoder or principal component analysis (PCA) to replace original features with new features
B
Run self-correlation on all features and remove highly correlated features
C
Cluster raw data using k-means and use sample data from each cluster to build a new dataset
D
Normalize all numerical values to be between 0 and 1
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2
Question
[Modeling] A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly dat
Select 2
Options
A
Set ForecastFrequency to W for weekly.
B
Set FeaturizationMethodName to filling.
C
Set PerformAutoML to true.
D
Set PerformHPO to true.
E
Set ForecastHorizon to 4.
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3
Question
[Data Engineering] A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?
Options
A
Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
B
Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
C
Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify.
Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
D
Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
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4
Question
[Modeling] A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question. A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.
B
Perform one-hot encoding on every possible option for each question of the survey.
C
Use Amazon Textract to create numeric features for each set of possible responses.
D
Perform binning on all the answers each respondent selected for each question.
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5
Question
[Modeling] A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Create an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.
B
Use the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.
C
Use the SageMaker Feature Store GetRecord API with the record identifier.
D
Create an Amazon Athena query to retrieve the data from the feature table.
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