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AWS Certified Machine Learning Engineer – Associate Exam (MLA-C01) - AWS Exam Questions
Last updated on June 20, 2026
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1
Question
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?
Which solution will meet these requirements with the LEAST operational effort?
Options
A
Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
B
Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
C
Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload.
Configure the pipeline as the target of the rule.
Configure the pipeline as the target of the rule.
D
Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
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2
Question
Case study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?
Which solution will meet this requirement with the LEAST operational effort?
Options
A
Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
B
Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C
Use AWS Glue DataBrew built-in features to oversample the minority class.
D
Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
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3
Question
An ML engineer is analyzing a classification dataset before training a model in Amazon SageMaker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre-training bias metric.
Which metric will meet this requirement?
Which metric will meet this requirement?
Options
A
Structural similarity index measure (SSIM)
B
Difference in proportions of labels (DPL)
C
Silhouette score
D
Mean squared error (MSE)
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4
Question
A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.
Which algorithm and hyperparameters should the company use to develop the model?
Which algorithm and hyperparameters should the company use to develop the model?
Options
A
Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm with contamination to set the expected proportion of anomalies.
B
Use k-means clustering with k to specify the number of clusters.
C
Use the Amazon SageMaker AI DeepAR algorithm with matching context length and prediction length hyperparameters.
D
Use the Amazon SageMaker AI XGBoost algorithm. Set the scale_pos_weight hyperparameter to adjust for class imbalance.
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5
Question
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
B
Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
C
Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
D
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
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