Amazon MLA-C01 Practice Exams
Last updated on Oct 21,2026- Exam Code: MLA-C01
- Exam Name: AWS Certified Machine Learning Engineer - Associate
- Certification Provider: Amazon
- Latest update: Oct 21,2026
You are a machine learning engineer at a biotech company developing a custom deep learning model for analyzing genomic data. The model relies on a specific version of TensorFlow with custom Python libraries and dependencies that are not available in the standard SageMaker environments. To ensure compatibility and flexibility, you decide to use the "Bring Your Own Container" (BYOC) approach with Amazon SageMaker for both training and inference.
Given this scenario, which steps are MOST IMPORTANT for successfully deploying your custom container with SageMaker, ensuring that it meets the company’s requirements?
- A . Package the model as a SageMaker-compatible file, upload it to Amazon S3, and use a pre-built SageMaker container for training, ensuring that the training job uses the custom environment
- B . Create a Docker container with the required environment, push the container image to Amazon ECR (Elastic Container Registry), and use SageMaker’s Script Mode to execute the training script within the container
- C . Build a Docker container with the required TensorFlow version and dependencies, push the
container image to Docker Hub, and reference the image in SageMaker when creating the training job - D . Deploy the model locally using Docker, then use the AWS Management Console to manually copy
the environment and model files to a SageMaker instance for training
You are a data scientist at a marketing agency tasked with creating a sentiment analysis model to analyze customer reviews for a new product. The company wants to quickly deploy a solution with minimal training time and development effort. You decide to leverage a pre-trained natural language processing (NLP) model and fine-tune it using a custom dataset of labeled customer reviews. Your team has access to both Amazon Bedrock and SageMaker JumpStart.
Which approach is the MOST APPROPRIATE for fine-tuning the pre-trained model with your custom dataset?
- A . Use SageMaker JumpStart to create a custom container for your pre-trained model and manually implement fine-tuning with TensorFlow
- B . Use SageMaker JumpStart to deploy a pre-trained NLP model and use the built-in fine-tuning functionality with your custom dataset to create a customized sentiment analysis model
- C . Use Amazon Bedrock to train a model from scratch using your custom dataset, as Bedrock is optimized for training large models efficiently
- D . Use Amazon Bedrock to select a foundation model from a third-party provider, then fine-tune the
model directly in the Bedrock interface using your custom dataset
What is uncertainty in the context of machine learning?
- A . The clarity of the model’s decision-making process
- B . The speed of the algorithm
- C . The amount of data available for training
- D . An imperfect outcome
You are a machine learning engineer at a tech company responsible for maintaining a recommendation engine that drives personalized content for users. The current model has been performing well, but you’ve recently developed a new model that incorporates additional features and advanced techniques. Before fully replacing the existing model, you want to evaluate the new model’s performance in a real-world environment. To do this, you decide to use A/B testing to compare the performance of the new model against the current model in terms of business outcomes.
Which of the following approaches is the MOST EFFECTIVE for conducting A/B testing for this use case?
- A . Deploy the new model as the primary endpoint and keep the current model as a fallback. Use AWS Lambda to route a percentage of traffic to the new model and compare the predictions manually to decide which model to keep
- B . Randomly assign a percentage of users to each model and measure engagement metrics, such as click-through rates and session duration, using Amazon CloudWatch to aggregate the results and determine the winning model
- C . Fully deploy the new model and observe its performance over time. If it performs worse than the old model, revert to the previous model and analyze the differences
- D . Deploy both models behind separate endpoints and use Amazon SageMaker Model Monitor to track key performance metrics like accuracy and latency, choosing the better model based on these metrics
Which aspect of AI does not directly involve machine learning?
- A . Data extraction in Amazon Textract.
- B . Intelligent search in Amazon Kendra.
- C . Document analysis in Amazon Comprehend.
- D . Rule-based automation without learning.
You are a data engineer responsible for monitoring the performance of a suite of machine learning models deployed across multiple environments at an e-commerce company. The models are used for various tasks, including recommendation engines, demand forecasting, and customer segmentation. To ensure that these models are performing optimally, you need to set up a centralized dashboard that allows stakeholders to monitor key performance metrics such as latency, accuracy, throughput, and resource utilization. The dashboard should be user-friendly, provide insights at a glance, and support both technical and non-technical users.
Which approach is MOST SUITABLE for setting up a dashboard to monitor the performance metrics of these ML models?
- A . Use Amazon QuickSight to create a visual dashboard that integrates data from Amazon CloudWatch Logs via Amazon S3, providing interactive charts and graphs that allow stakeholders to drill down into specific metrics as needed
- B . Create a custom web application that pulls data from CloudWatch Logs, generating real-time visualizations of performance metrics. Embed the application within the company’s internal portal for easy access
- C . Set up a CloudWatch Dashboard to aggregate and display performance metrics such as CPU utilization, memory usage, and latency from various services. Use Amazon SNS to send daily reports based on these metrics to stakeholders
- D . Use AWS Lambda to query CloudWatch Logs and send the results to an S3 bucket daily, where stakeholders can manually review the metrics and create their own visualizations using spreadsheet software
You are a machine learning engineer responsible for deploying a customer churn prediction model using Amazon SageMaker into production at a telecommunications company. The model is critical for proactive customer retention efforts, so maintaining high availability and reliability is essential. Given the model’s importance, you must implement best practices for deployment, including versioning and rollback strategies, to ensure that any issues with the new model version can be quickly addressed without impacting the business.
Which of the following approaches BEST exemplifies deployment best practices in this scenario?
- A . Version the model by tagging the new version, deploy it to production, and use weighted traffic splitting to send a small percentage of traffic to the new model. If no issues are detected, gradually increase traffic to the new version
- B . Use a blue/green deployment strategy to deploy the new model version in parallel with the existing version, allowing you to switch traffic to the new model gradually and roll back if necessary
- C . Deploy the new model version to a staging environment, test it thoroughly, and then manually replace the existing production model. If any issues occur, update the model in staging and redeploy
- D . Deploy the new model version directly to production, replacing the existing model, and monitor its
performance closely. If issues arise, retrain the model immediately and redeploy
You are a machine learning engineer at a healthcare company responsible for developing and deploying an end-to-end ML workflow for predicting patient readmission rates. The workflow involves data preprocessing, model training, hyperparameter tuning, and deployment. Additionally, the solution must support regular retraining of the model as new data becomes available, with minimal manual intervention. You need to select the right solution to orchestrate this workflow efficiently while ensuring scalability, reliability, and ease of management.
Given these requirements, which of the following options is the MOST SUITABLE for orchestrating your ML workflow?
- A . Implement the entire ML workflow using Amazon SageMaker Pipelines, which provides integrated orchestration for data processing, model training, tuning, and deployment
- B . Use AWS Step Functions to define and orchestrate each step of the ML workflow, integrate with SageMaker for model training and deployment, and leverage AWS Lambda for data preprocessing tasks
- C . Leverage Amazon EC2 instances to manually execute each step of the ML workflow, use Amazon RDS for storing intermediate results, and deploy the model using Amazon SageMaker endpoints
- D . Use AWS Glue for data preprocessing, Amazon SageMaker for model training and tuning, and
manually deploy the model to an Amazon EC2 instance for inference
You are a data scientist at an e-commerce company working to develop a recommendation system for customers. After building several models, including collaborative filtering, content-based filtering, and a deep learning model, you find that each model excels in different scenarios. For example, the collaborative filtering model works well for returning customers with rich interaction data, while the content-based filtering model performs better for new customers with little interaction history. Your goal is to combine these models to create a recommendation system that provides more accurate and personalized recommendations across all customer segments.
Which of the following strategies is the MOST LIKELY to achieve this goal?
- A . Implement a hybrid model that combines the predictions of collaborative filtering, content-based filtering, and deep learning using a weighted average, where weights are based on model performance for different customer segments
- B . Apply boosting by sequentially training the collaborative filtering, content-based filtering, and deep learning models, where each model corrects the errors of the previous one
- C . Use stacking, where the predictions from the collaborative filtering and content-based filtering models are fed into a deep learning model as inputs, allowing the deep learning model to make the final recommendation
- D . Use a bagging approach to train multiple instances of the deep learning model on different subsets of the data and average their predictions to improve overall performance
You are a Senior ML Engineer at a global logistics company that heavily relies on machine learning models for optimizing delivery routes, predicting demand, and detecting anomalies in real-time. The company is rapidly expanding, and you are tasked with building a maintainable, scalable, and cost-effective ML infrastructure that can handle increasing data volumes and evolving model requirements. You must implement best practices to ensure that the infrastructure can support ongoing development, deployment, monitoring, and scaling of multiple models across different regions.
Which of the following strategies should you implement to create a maintainable, scalable, and cost-effective ML infrastructure for your company using AWS services? (Select three)
- A . Provision fixed resources for each model to avoid unexpected costs, ensuring that the infrastructure is always available for each model
- B . Store all model artifacts and data in Amazon CodeCommit for version control and managing changes over time
- C . Use a monolithic architecture to manage all machine learning models in a single environment, simplifying management and reducing overhead
- D . Store all model artifacts and data in Amazon S3, and use versioning to manage changes over time, ensuring that models can be easily rolled back if needed
- E . Implement a microservices-based architecture with Amazon SageMaker endpoints, where each model is deployed independently, allowing for isolated scaling and updates
- F . Utilize infrastructure as code (IaC) with AWS CloudFormation to automate the deployment and management of ML resources, making it easy to replicate and scale infrastructure across regions