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: Jan 06, 2026

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Question 1

A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.

What does the Specialist need to do?

A : ndle the NVIDIA drivers with the Docker image.

B : ild the Docker container to be NVIDIA-Docker compatible.

C : ganize the Docker container's file structure to execute on GPU instances.

D : t the GPU flag in the Amazon SageMaker CreateTrainingJob request body

Answer: B

Question 2

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

A : ll the CreateNotebookInstanceLifecycleConfig API operation

B : eate a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

C : op and then restart the SageMaker notebook instance

D : ll the UpdateNotebookInstanceLifecycleConfig API operation

Answer: C

Question 3

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A : ply dimensionality reduction by using the principal component analysis (PCA) algorithm.

B : op the features with low correlation scores by using a Jupyter notebook.

C : ply anomaly detection by using the Random Cut Forest (RCF) algorithm.

D : ncatenate the features with high correlation scores by using a Jupyter notebook.

Answer: C

Question 4

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

Question 5

A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker. The ML specialist implements the algorithm in a Docker container that is supported by SageMaker. How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?

A : ecify the server argument in the ENTRYPOINT instruction in the Dockerfile

B : ecify the training program in the ENTRYPOINT instruction in the Dockerfile

C : clude the path to the training data in the docker build command when packaging the container.

D : a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory

Answer: B

Page: 1 / 79

Total 392 Questions | Updated On: Jan 06, 2026

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