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: Apr 01, 2026

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Question 1

A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup. Which solution will meet these requirements?

A : File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.

B : eate an Amazon FSx for Lustre file system. Link the file system to the S3 buckets.

C : eate an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the training instances

D : FastFile mode in SageMaker to stream the files on demand from the S3 buckets

Answer: D

Question 2

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

New one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic.

Which solution will meet these requirements?

A : A/B testing

B : Canary release

C : Shadow deployment

D : Blue/green deployment

Answer: D

Question 3

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 4

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent

How should the Specialist frame this business problem'?

A : reaming classification

B : nary classification

C : lti-category classification

D : gression classification

Answer: A

Question 5

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?

A : n self-correlation on all features and remove highly correlated features

B : rmalize all numerical values to be between 0 and 1

C : an autoencoder or principal component analysis (PCA) to replace original features with new features

D : uster raw data using k-means and use sample data from each cluster to build a new dataset

Answer: B

Page: 1 / 79

Total 392 Questions | Updated On: Apr 01, 2026

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