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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 5 )

Updated On: 9-Feb-2026
Page 5 of 47
PDF with 230 Questions

A company has a team of data scientists who use Amazon SageMaker AI 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 AI notebook instance.

The company needs to centralize management of the team's permissions.

Which solution will meet this requirement?

  1. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
  2. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
  3. 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. 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.

Answer(s): A

Explanation:

By creating a single IAM role with the required permissions and attaching it to each SageMaker notebook instance, the company can centralize permission management. This solution ensures that all notebook instances share the same permissions, eliminating the need to manage permissions individually for each instance or user. It aligns with AWS best practices for role-based access control and reduces operational overhead.



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. Accuracy
  2. Area Under the ROC Curve (AUC)
  3. F1 score
  4. Mean absolute error (MAE)

Answer(s): D

Explanation:

For regression tasks like predicting apartment prices, Mean Absolute Error (MAE) is an appropriate metric because it measures the average magnitude of errors between the predicted and actual values. Unlike classification metrics (e.g., Accuracy, AUC, F1 score), MAE provides direct insight into how well the model's predictions align with the actual prices, making it suitable for this use case.



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. Introduce early stopping.
  2. Increase the size of the test set.
  3. Increase the learning rate.
  4. Decrease the learning rate.

Answer(s): D

Explanation:

The oscillating pattern in training and validation loss suggests that the learning rate is too high, causing the optimization process to overshoot the minimum during gradient descent. By decreasing the learning rate, the training process will take smaller steps toward the optimal solution, stabilizing the loss values and improving the model's ability to converge to a minimum.



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. 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. 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. 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. 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.

Answer(s): A

Explanation:

Using Amazon Athena with a CREATE TABLE AS SELECT (CTAS) statement is the most efficient solution with the least operational overhead. Athena allows direct querying of data stored in S3 using SQL, without the need for moving or replicating data. The CTAS statement can be used to create a new table organized by the transaction date, enabling efficient querying of the CSV objects. This approach avoids the complexity and additional costs associated with replication or setting up separate processing pipelines.



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

Answer(s): D

Explanation:

The AWS Glue FindMatches transform is specifically designed to detect duplicates in large, unstructured datasets with minimal code development. It uses machine learning to identify similar records across datasets, even when they do not match exactly. FindMatches is easy to use, requires little configuration, and integrates seamlessly with AWS Glue for pre-processing tasks, making it the best solution with the least operational and coding effort.






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