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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Actual Questions
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 4 )

Updated On: 8-Aug-2026
Page 4 of 33
PDF with 270 Questions

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.



A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.

Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  1. Spot Instances
  2. Reserved Instances
  3. On-Demand Instances
  4. Dedicated Instances

Answer(s): A

Explanation:

Spot Instances are the most cost-effective option for batch jobs that can tolerate interruptions. They offer significant discounts compared to On-Demand Instances because they utilize unused EC2 capacity. Since the job runs on the weekend, lasts only 90 minutes, and can handle interruptions, Spot Instances are ideal for this use case. This purchasing option minimizes costs while meeting the company's requirements.



An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.

Which solution will meet this requirement with the LEAST development effort?

  1. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  2. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account
  3. Use AWS DataSync to replicate the model from Account A to Account B.
  4. Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model.

Answer(s): B

Explanation:

Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.



An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.

Which solutions will mitigate this problem? (Choose two.)

  1. Enable early stopping on the model.
  2. Increase dropout in the layers.
  3. Increase the number of layers.
  4. Increase the number of neurons.
  5. Investigate and reduce the sources of model bias.

Answer(s): A,B

Explanation:

Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.



A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

Which solution will meet these requirements?

  1. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
  2. Use a custom Amazon SageMaker AI notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
  3. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra.
    Query Amazon Kendra to perform the semantic searches.
  4. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

Answer(s): C

Explanation:

Amazon Kendra is an AI-powered search service designed for semantic search use cases. It allows ingestion of documents from an Amazon S3 bucket using the Amazon Kendra S3 connector. Once the documents are ingested, Kendra enables semantic searches with its built-in capabilities, removing the need to manually generate embeddings or manage a vector database. This approach is efficient, requires minimal operational effort, and meets the requirements for a Retrieval Augmented Generation (RAG) application.



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