Amazon MLA-C01 Practice Exams
Last updated on Oct 22,2026- Exam Code: MLA-C01
- Exam Name: AWS Certified Machine Learning Engineer - Associate
- Certification Provider: Amazon
- Latest update: Oct 22,2026
You are a machine learning engineer at a fintech company responsible for maintaining the ML infrastructure that powers real-time credit scoring for loan applications. The system must handle high volumes of requests with low latency and be resilient to any failures. To ensure the infrastructure meets the company’s performance and reliability requirements, you need to monitor key performance metrics related to scalability, availability, utilization, throughput and fault tolerance.
Which combination of metrics and monitoring strategies is the MOST EFFECTIVE for ensuring the ML infrastructure meets these requirements?
- A . Monitor CPU and memory utilization to ensure that compute resources are not overburdened, track request throughput to measure the number of predictions per second, and use auto-scaling policies to maintain high availability and scalability during traffic spikes
- B . Track model accuracy and precision to ensure that predictions are correct, monitor disk space utilization to prevent storage overflows, and manually scale the infrastructure during peak usage periods
- C . Monitor training loss and validation accuracy to track model performance, measure network bandwidth to ensure efficient data transfer, and use Amazon CloudWatch alarms to automatically restart failed instances
- D . Measure latency to ensure predictions are delivered quickly, monitor the number of failed requests to assess fault tolerance, and use scheduled scaling to adjust resources based on anticipated demand
A data scientist needs to identify fraudulent user accounts for a company’s ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company’s application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A . Create a FindMatches machine learning transform in AWS Glue.
- B . Run the built-in FindDuplicates Amazon Athena query.
- C . Create an AWS Glue crawler to infer duplicate accounts in the source data.
- D . Search for duplicate accounts in the AWS Glue Data Catalog.
You are a data scientist at a healthcare company working on deploying a machine learning model that predicts patient outcomes based on real-time data from wearable devices. The model needs to be containerized for easy deployment and scaling across different environments, including development, testing, and production. The company wants to ensure that container images are managed efficiently, securely, and consistently across all environments.
Given these requirements, which combination of AWS services is the MOST SUITABLE for building, storing, deploying, and maintaining the containerized ML solution?
- A . Use Docker Hub to store the container images, Amazon EKS for orchestrating the containers, and AWS Lambda to trigger updates to the containers when new images are pushed
- B . Use Amazon ECR to store the container images, Amazon EKS for orchestrating the containers, and AWS CodePipeline for automating the CI/CD pipeline, ensuring that updates to the model are seamlessly deployed
- C . Use Amazon ECR to store container images, manually deploy containers on Amazon EC2 instances, and use AWS CloudFormation to manage the infrastructure configuration
- D . Use Amazon ECS to manage and deploy the containerized model, Amazon S3 to store container images, and manually push updates to the containers using the AWS CLI
You are a machine learning engineer at a company building a recommendation system using deep learning. The system is based on a neural network architecture implemented in TensorFlow. Your team wants to train the model on a large dataset stored in Amazon S3 and leverage the scalability of Amazon SageMaker. You plan to use SageMaker script mode to customize the training process while taking advantage of SageMaker’s managed services.
Which of the following actions is the MOST LIKELY to help you successfully train the model using SageMaker script mode with TensorFlow?
- A . Package your TensorFlow model as a .tar.gz file, upload it to Amazon S3, and use SageMaker script mode to directly deploy the model for training
- B . Use SageMaker script mode to automatically convert your Python script into a SageMaker Estimator object without specifying the TensorFlow framework version
- C . Create a custom Docker container with TensorFlow installed and push it to Amazon ECR, then use SageMaker script mode to launch the training job
- D . Write a training script in Python that adheres to TensorFlow’s Estimator API, upload it to an S3 bucket, and use SageMaker script mode with the built-in TensorFlow container to execute the script
You are a data scientist working on a machine learning project to predict customer lifetime value (CLV) for an e-commerce company. Before deploying a complex model like a deep neural network, you need to establish a performance baseline to measure the effectiveness of your advanced models. You decide to use Amazon SageMaker to create this baseline efficiently. The goal is to build a simple model that can be easily implemented and provide a reference point for evaluating the performance of more sophisticated models later.
Which of the following approaches is the MOST EFFECTIVE for creating a performance baseline using Amazon SageMaker?
- A . Train a basic linear learner model using Amazon SageMaker, focusing on key features like customer age, purchase frequency, and average order value, to establish a baseline for CLV prediction
- B . Implement SageMaker Autopilot to automatically explore various models and select the best one as the baseline, allowing you to skip manual model selection
- C . Deploy a SageMaker BlazingText model to create word embeddings from customer reviews, which can be used as a baseline for evaluating CLV predictions
- D . Use SageMaker JumpStart to deploy a pre-trained model for customer segmentation, which can serve as a baseline for your CLV prediction model
You are a Data Scientist working for a retail company and you have been tasked with developing a demand forecasting model using Amazon SageMaker. The company requires the model to be highly accurate to optimize inventory levels, but you also need to consider constraints on training time and cost due to budget limitations. You have access to multiple SageMaker instance types and options like spot instances to reduce costs, but you must balance these factors against the need for a performant model. Your goal is to choose a configuration that provides an acceptable tradeoff between model performance, training time, and cost.
Which of the following strategies should you consider when balancing model performance, training time, and cost in this scenario using Amazon SageMaker? (Select two)
- A . Implement distributed training across multiple smaller instances to balance training time and cost while maintaining model performance
- B . Deploy multiple models with different instance types simultaneously, choosing the one that completes training first, regardless of performance or cost
- C . Optimize hyperparameters using Amazon SageMaker’s automatic model tuning (hyperparameter optimization) to improve performance, while using spot instances to reduce cost
- D . Use a smaller instance type to save on costs, and accept longer training times, as model accuracy is not the highest priority
- E . Use the largest available instance type to minimize training time, regardless of cost, ensuring that the model is trained as quickly as possible
You are a data scientist at a healthcare company that has deployed a machine learning model to predict patient readmission rates. The model plays a crucial role in optimizing patient care and managing hospital resources. After several months in production, the medical team has noticed that the model’s predictions seem less accurate than before, leading to concerns about data quality and model performance. To ensure that the model continues to deliver reliable predictions, you need to implement techniques to monitor both data quality and model performance continuously.
Which of the following approaches is the MOST EFFECTIVE for monitoring data quality and model performance in this scenario?
- A . Implement data validation rules to check for missing values, outliers, and distribution changes in the input data before feeding it to the model, and use model performance metrics such as accuracy and F1 score to monitor the model’s output
- B . Schedule periodic retraining of the model on the latest data to ensure it remains accurate, without additional monitoring of data quality or performance metrics
- C . Set up a dashboard that tracks model performance metrics, such as precision, recall, and AUC, and use version control to monitor changes in the model’s code and training data over time
- D . Perform manual checks on a sample of the input data each week to ensure data quality and manually track model performance metrics in a spreadsheet for analysis
You are an ML Engineer at a financial services company tasked with deploying a machine learning model for real-time fraud detection in production. The model requires low-latency inference to ensure that fraudulent transactions are flagged immediately. However, you also need to conduct extensive testing and experimentation in a separate environment to fine-tune the model and validate its performance before deploying it. You must provision compute resources that are appropriate for both environments, balancing performance, cost, and the specific needs of testing and production.
Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment? (Select two)
- A . Leverage AWS Inferentia accelerators in the production environment to meet high throughput and low latency requirements
- B . Provision CPU-based instances in both production and test environments to reduce costs, as CPU instances are generally cheaper than GPU instances
- C . Use GPU-based instances in both production and test environments to ensure that the model inference and testing are both performed at maximum speed, regardless of cost
- D . Use CPU-based instances in the test environment to save on costs during experimentation
- E . Provision identical instances in both production and test environments to ensure consistent
performance between the two, eliminating the risk of discrepancies during deployment
How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?
- A . K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions
- B . K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data
- C . K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering
- D . K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples
How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?
- A . K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions
- B . K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data
- C . K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering
- D . K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples