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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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[Data Engineering] A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that the model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?
Options
A
Create a line chart with the weekly mean absolute error (MAE) of the model.
B
Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
C
Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
D
Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
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2
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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3
Question
[Exploratory Data Analysis] A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes
public transit in New York City. One of the random variables is discrete, and represents the number of
minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
public transit in New York City. One of the random variables is discrete, and represents the number of
minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
Options
A
Normal distribution
B
Poisson distribution ,
C
Binomial distribution
D
Uniform distribution
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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
[Data Engineering] An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)
Select 3
Options
A
An 1AM role that allows access to the specific S3 bucket
B
An S3 bucket owner that matches the notebook owner
C
An AWS KMS key policy that allows access to the customer master key (CMK)
D
A permissive S3 bucket policy
E
A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
F
A SageMaker notebook security group that allows access to Amazon S3
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