Free MLA-C01: AWS Certified Machine Learning Engineer - Associate Exam Questions and Answers

Last updated: September 19, 2026

Exam code

MLA-C01

Provider

Amazon

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1000+

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1. Question #1

   A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance. Which solution will meet these requirements in the LEAST amount of time?

   • A. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
   • B. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
   • C. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
   • D. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.
   Reveal Answer

2. Question #2

   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?

   • A. Place the instances in the same VPC subnet. Store the data in a different AWS Region 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 the same AWS Region but in a different Availability Zone from where the instances are deployed.
   Reveal Answer

3. Question #3

   A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support. Which modeling approach should the company use to meet this requirement?

   • A. Anomaly detection
   • B. Linear regression
   • C. Logistic regression
   • D. Semantic segmentation
   Reveal Answer

4. Question #4

   A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable. Which solution will provide this information with the LEAST development effort?

   • A. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
   • B. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
   • C. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
   • D. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
   Reveal Answer

5. Question #5

   A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running. How should the company deploy the model on Amazon SageMaker to meet these requirements?

   • A. Use a multi-model serverless endpoint. Enable caching.
   • B. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
   • C. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
   • D. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
   Reveal Answer

6. Question #6

   A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment. The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand. How should the company deploy the model into production to meet these requirements?

   • A. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
   • B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
   • C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
   • D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inference
   • E. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.
   Reveal Answer

7. Question #7

   A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product. Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

   • A. Use Amazon Rekognition to analyze sentiments of the chat conversations.
   • B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
   • C. Use Amazon Comprehend to analyze sentiments of the chat conversations.
   • D. Use random forests to classify sentiments of the chat conversations.
   Reveal Answer

8. Question #8

   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.
   Reveal Answer

9. Question #9

   A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor. Which solution will meet this requirement?

   • A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
   • B. Configure an Amazon Q Business retriever to exclude the competitor's name.
   • C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
   • D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
   Reveal Answer

10. Question #10

    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 in the same Region. Which solution will meet this requirement with the LEAST development effort?

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