Free Amazon MLS-C01 Exam Questions

Become Amazon Certified with updated MLS-C01 exam questions and correct answers

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Total 392 Questions | Updated On: Feb 19, 2026
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Question 1

A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier. The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks. How can the ML specialist meet these requirements in the LEAST amount of time?


Answer: B
Question 2

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data
Which type of data repository is the MOST cost-effective solution?


Answer: C
Question 3

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)


Answer: A,B
Question 4

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?


Answer: B
Question 5

A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job. Which combination of steps should the data engineer take to meet these requirements? (Choose three.)


Answer: A,D,F
Page:    1 / 79      
Total 392 Questions | Updated On: Feb 19, 2026
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