Free Amazon MLA-C01 Exam Questions

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

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Total 339 Questions | Updated On: Dec 06, 2025

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Question 1

Why is it important to estimate the amount of uncertainty in an ML model?

A : improve the model's computational efficiency

B : increase the speed of data processing

C : reduce the amount of training data needed

D : avoid potential misinterpretations of life and property

Answer: D

Question 2

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor. Which solution will meet this requirement?

A : Configure the competitor's name as a blocked phrase in Amazon Q Business.

B : Configure an Amazon Q Business retriever to exclude the competitors name.

C : Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

D : Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Answer: A

Question 3

Which solution will meet these requirements?

A : the SageMaker Feature Store GetRecord API with the record identifier.

B : the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.

C : eate an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.

D : eate an Amazon Athena query to retrieve the data from the feature table.

Answer: A

Question 4

What characterizes a simple machine learning model?

A : w accuracy but high explainability

B : gh accuracy and high explainability

C : w accuracy and low explainability

D : gh accuracy but low explainability

Answer: A

Question 5

A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

A : Hyperbaric!

B : Grid search

C : Bayesian optimization

D : Random search

Answer: A

Page: 1 / 68

Total 339 Questions | Updated On: Dec 06, 2025

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