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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 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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2
Question
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
Options
A
Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
B
Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
C
Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
D
Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
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3
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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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 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?
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?
Options
A
Use the AWS Glue FindMatches transform to detect duplicates.
B
Use Amazon QuickSight ML Insights to build a custom deduplication model.
C
Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
D
Use Amazon Mechanical Turk jobs to detect duplicates.
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