Free Amazon MLS-C01 Exam Questions

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

Page: 1 / 79

Total 392 Questions | Updated On: Feb 19, 2026

Add To Cart

Question 1

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 fraudulent.

How should the Specialist frame this business problem?

A : reaming classification

B : nary classification

C : lti-category classification

D : gression classification

Answer: C

Question 2

A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric. The ML specialist varies the number and size of the MLP's hidden layers, but the results do not improve significantly.

Which solution will improve recall in the LEAST amount of time?

A : Add class weights to the MLP's loss function, and then retrain.

B : Gather more data by using Amazon Mechanical Turk, and then retrain.

C : Train a k-means algorithm instead of an MLP.

D : Train an anomaly detection model instead of an MLP.

Answer: A

Question 3

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A : Linear regression is inappropriate. The residuals do not have constant variance.

B : Linear regression is inappropriate. The underlying data has outliers.

C : Linear regression is appropriate. The residuals have a zero mean.

D : Linear regression is appropriate. The residuals have constant variance.

Answer: D

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 gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns

Using this dataset for training, the Data Science team trained a random forest model that converged with over 99?curacy on the training set However, the prediction results on a test dataset were not satisfactory.

Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A : more deep trees to the random forest to enable the model to learn more features.

B : dicate a copy of the samples in the test database in the training dataset

C : nerate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

D : ange the cost function so that false negatives have a higher impact on the cost value than false positives

E : ange the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: C,D

Page: 1 / 79

Total 392 Questions | Updated On: Feb 19, 2026

Add To Cart