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Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

Exam Code:
MLS-C01
Exam Name:
AWS Certified Machine Learning - Specialty
Certification:
AWS Certified Specialty
Vendor:
Amazon Web Services
Questions:
330
Last Updated:
Oct 16, 2026
Exam Status:
Stable
Amazon Web Services MLS-C01

MLS-C01: AWS Certified Specialty Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Machine Learning - Specialty Questions and Answers

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Question 1

A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.

A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.

Which labeling approach will help the company improve this model?

Options:

A.

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

B.

Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

C.

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

D.

Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

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Question 2

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?  

Options:

A.

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.

B.

Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.

C.

Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.

D.

Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.

Question 3

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

Options:

A.

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

B.

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

C.

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

D.

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

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