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MLA-C01 Exam Practice Questions

AWS Certified Machine Learning Engineer – Associate

MLA-C01 English PDF practice questions: 271 questions in source order, with options supported by the source format, answers and available explanations. Access covers this exam only. One-time payment; no automatic renewal.

Exam overview

For ML engineers preparing data, developing models, deploying workflows and maintaining ML workloads on AWS.

The MLA-C01 English version has ended. Listed localized versions remain during the MLA-C02 transition; beta details differ.

Exam code
MLA-C01
Official exam / credential
AWS Certified Machine Learning Engineer – Associate
Exam format
Single-answer and multiple-response questions
Exam duration
130 minutes
Official exam questions
65 questions
Official exam languages
Japanese, Korean, Simplified Chinese

Official exam languages and practice-question file languages are listed separately. Confirm the exam version and available languages when booking with the provider.

Official sources: Provider reference
Information checked: 2026-10-20

Supported languages

  • English
  • Simplified Chinese
Try before you buy

First 20 practice questions

271 total questions

Questions follow the source order. Expand each answer to see its explanation.

Disclaimer: All questions on ExamPico are compiled from internet sources and are provided for study reference only.

Question 16

Question#16Topic1

A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

  1. A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
  2. B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
  3. C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
  4. D. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.
Show answer

Answer

A

Question 17

Question#17Topic1

An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

  1. A. Accuracy
  2. B. Area Under the ROC Curve (AUC)
  3. C. F1 score
  4. D. Mean absolute error (MAE)
Show answer

Answer

D

Question 18

Question#18Topic1

An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

  1. A. Introduce early stopping.
  2. B. Increase the size of the test set.
  3. C. Increase the learning rate.
  4. D. Decrease the learning rate.
Show answer

Answer

D

Question 19

Question#19Topic1

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?

  1. A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.
  2. B. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
  3. C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.
  4. D. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
Show answer

Answer

A

Question 20

Question#20Topic1

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

  1. A. Use Amazon Mechanical Turk jobs to detect duplicates.
  2. B. Use Amazon QuickSight ML Insights to build a custom deduplication model.
  3. C. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
  4. D. Use the AWS Glue FindMatches transform to detect duplicates.
Show answer

Answer

D

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