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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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1
Question
[Exploratory Data Analysis] A company wants to segment a large group of customers into subgroups based on shared characteristics. The company’s data scientist is planning to use the Amazon SageMaker built-in k- means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.
Which data visualization approach will MOST accurately determine the optimal value of k?
Which data visualization approach will MOST accurately determine the optimal value of k?
Options
A
Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values.
The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
B
Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.
C
Run the k-means clustering algorithm for a range of
D
Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.
E
The optimal value of k is the point after which the curve starts decreasing in a linear fashion.
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2
Question
[Modeling] A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling
for its SageMaker instances to support workload changes. During testing, the team notices that
additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?
for its SageMaker instances to support workload changes. During testing, the team notices that
additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?
Options
A
Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
B
Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.
C
Increase the cooldown period for the scale-out activity.
D
Replace the current endpoint with a multi-model endpoint using SageMaker.
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3
Question
[Data Engineering] A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.
What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?
What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?
Options
A
Polynomial transformation
B
Exponential transformation
C
Sinusoidal transformation
D
Logarithmic transformation
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4
Question
[Modeling] A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameter that will generate the most accurate model.
Which approach will meet these requirements with the LEAST operational overhead?
Which approach will meet these requirements with the LEAST operational overhead?
Options
A
Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
B
Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster.
Apply k-fold cross-validation methods to the algorithm.
Apply k-fold cross-validation methods to the algorithm.
C
Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.
D
Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.
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5
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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