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Exam Code: MLA-C01
Exam Title: AWS Certified Machine Learning Engineer - Associate
Vendor: Amazon-Web-Services
Exam Questions: 0
Last Updated: March 9th,2026

Question 1

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

A. Concept drift occurred in the underlying customer data that was used for predictions.
B. The model was not sufficiently complex to capture all the patterns in the original baseline data.
C. The original baseline data had a data quality issue of missing values.
D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Correct Answer:A

Question 2

An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use TensorBoard to monitor the training jo
B. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topi
C. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
D. Use Amazon CloudWatch default metrics to gain insights about the training jo
E. Use themetrics to invoke an AWS Lambda function to initiate the predefined actions.
F. Expand the metrics in Amazon CloudWatch to include the gradients in each training ste
G. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
H. Use SageMaker Debugger built-in rules to monitor the training jo
I. Configure the rules to initiate the predefined actions.

Correct Answer:D

Question 3

A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
B. Use processing steps in SageMaker Pipeline
C. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
D. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
E. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.

Correct Answer:C

Question 4

A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?

A. Add resource tagging by editing the SageMaker user profile in the SageMaker domai
B. Configure AWS Cost Explorer to send an alert when the threshold is reached.
C. Add resource tagging by editing the SageMaker user profile in the SageMaker domai
D. Configure AWS Budgets to send an alert when the threshold is reached.
E. Add resource tagging by editing each user's IAM profil
F. Configure AWS Cost Explorer to send an alert when the threshold is reached.
G. Add resource tagging by editing each user's IAM profil
H. Configure AWS Budgets to send an alert when the threshold is reached.

Correct Answer:B

Question 5

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangle
B. Automate the process by using Data Wrangler jobs.
C. Use Amazon SageMaker notebooks for each data processing ste
D. Automate the process by using Amazon EventBridge.
E. Process data at each step by using AWS Lambda function
F. Automate the process by using AWS Step Functions and Amazon EventBridge.
G. Use Amazon SageMaker Pipelines to create a pipeline of data processing step
H. Automate the pipeline by using Amazon EventBridge.

Correct Answer:D