| Exam Code | MLA-C01 |
| Exam Name | AWS Certified Machine Learning Engineer - Associate |
| Questions | 241 Questions Answers With Explanation |
| Update Date | June 13,2026 |
| Price |
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A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model?
A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
B. Implement padding to ensure that all color feature vectors have the same length.
C. Perform dimensionality reduction on the color categories.
D. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS. The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10–15 minutes. Which deployment configuration meets these requirements?
A. CodeDeployDefault.LambdaLinear10PercentEvery10Minutes
B. CodeDeployDefault.ECSAllAtOnce
C. CodeDeployDefault.ECSCanary10Percent15Minutes
D. CodeDeployDefault.LambdaCanary10Percent15Minutes
A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker AI from collecting metadata from the training jobs?
A. Opt out of metadata tracking for any training job that is submitted.
B. Ensure that training jobs are running in a private subnet in a custom VPC.
C. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
D. Reconfigure the training jobs to use only AWS Nitro instances.
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements?
A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
B. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.
C. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
D. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure crossaccount access to the Data Catalog.
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement?
A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity.
B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters.
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations.
D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100.
A company uses the Amazon SageMaker AI Object2Vec algorithm to train an ML model. The model performs well on training data but underperforms after deployment. The company wants to avoid overfitting the model and maintain the model's ability to generalize. Which solution will meet these requirements?
A. Decrease the early_stopping_patience hyperparameter.
B. Increase the mini_batch_size hyperparameter.
C. Decrease the dropout rate.
D. Increase the number of epochs.
A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?
A. Amazon Elastic Container Registry (Amazon ECR)
B. Model packages from Amazon SageMaker Marketplace
C. Amazon SageMaker ML Lineage Tracking
D. Amazon SageMaker Model Registry
A company launches a feature that predicts home prices. An ML engineer trained a regression model using the SageMaker AI XGBoost algorithm. The model performs well on training data but underperforms on real-world validation data. Which solution will improve the validation score with the LEAST implementation effort?
A. Create a larger training dataset with more real-world data and retrain.
B. Increase the num_round hyperparameter.
C. Change the eval_metric from RMSE to Error.
D. Increase the lambda hyperparameter.
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses. Which solution will meet these requirements?
A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public
access enabled in the primary account. Create a VPC peering connection between the
accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.
An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket. The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours. Which solution will meet these requirements?
A. Create an AWS Lambda function that runs one time each week to poll the S3 bucket for
new files. Invoke the Lambda function asynchronously. Configure the Lambda function to
start the pipeline if the function detects new data.
B. Create an Amazon CloudWatch rule that runs on a schedule to start the pipeline every 30 days.
C. Create an S3 Lifecycle rule to start the pipeline every time a new object is uploaded to the S3 bucket.
D. Create an Amazon EventBridge rule to start an AWS Step Functions TrainingStep every time a new object is uploaded to the S3 bucket.
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. Which solution will improve the model's accuracy in the LEAST amount of time?
A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training
dataset.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
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?
A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the
dataset accordingly.
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 the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and display the results on a dashboard. An ML engineer creates a bias monitoring job. How should the ML engineer capture bias metrics to display on the dashboard?
A. Capture AWS CloudTrail metrics from SageMaker Clarify.
B. Capture Amazon CloudWatch metrics from SageMaker Clarify.
C. Capture SageMaker Model Monitor metrics from Amazon EventBridge.
D. Capture SageMaker Model Monitor metrics from Amazon SNS.
An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset. The ML engineer needs to make changes before running the hyperparameter grid search again. Which changes will improve the model's performance? (Select TWO.)
A. Increase the model complexity by increasing the number of features in the dataset.
B. Decrease the model complexity by reducing the number of features in the dataset.
C. Decrease the model complexity by reducing the number of samples in the dataset.
D. Increase the value of the L2 regularization parameter.
E. Decrease the value of the L2 regularization parameter.
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 costeffectively?
A. Spot Instances
B. Reserved Instances
C. On-Demand Instances
D. Dedicated Instances
A retail company is analyzing customer purchase data to develop personalized product recommendations. The company wants to use Amazon SageMaker Clarify to assess fairness metrics across different customer groups to avoid potential bias in the recommendation system. The recommendation system needs to identify if certain customer segments are underrepresented in the training data. The company needs to choose a pre-training bias metric in SageMaker Clarify. Which metric meets these requirements?
A. Prediction distribution skew
B. Feature attribution bias
C. Class imbalance ratio
D. Model performance gap
A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic. Which solution will meet these requirements?
A. Configure auto scaling on the SageMaker AI endpoint.
B. Create a new SageMaker AI endpoint. Deploy the model to the new endpoint.
C. Use SageMaker Neo to optimize the model for inference.
D. Attach an Auto Scaling group to the SageMaker AI endpoint.
A company has a team of data scientists who use Amazon SageMaker 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 notebook instance. The company needs to centralize management of the team's permissions. Which solution will meet this requirement?
A. Create a single IAM role that has the necessary permissions. Attach the role to each
notebook instance that the team uses.
B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
D. 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.
An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time. Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent. Which solution will meet this requirement?
A. Compilation
B. Speculative decoding
C. Quantization
D. Fast model loading
A streaming media company uses a churn risk model to assess the churn risk of its premium tier customers. Each month, the company runs an aggregation job on individual customers’ streaming data and uploads the user engagement features to an Amazon S3 bucket. The company manually re-trains the churn risk model with the user engagement data. The current process requires manual intervention and is time-consuming. The company needs a solution that automatically re-trains the churn prediction model with the most recent data. Which solution will meet these requirements with the SHORTEST delay?
A. Set up an Amazon EventBridge rule to run an Amazon Elastic Container Service
(Amazon ECS) task hourly for model re-training. Configure the ECS task to use the most
recent data from the S3 bucket.
B. Configure the S3 bucket to invoke an AWS Lambda function that re-trains the model.
C. Create a pipeline in Amazon SageMaker Pipelines for re-training. Configure an Amazon EventBridge rule to monitor S3 PutObject creation events and invoke the pipeline.
D. Create a pipeline in Amazon SageMaker Pipelines for re-training. Configure a pipeline schedule to re-train the model.
