AWS Certified Machine Learning Engineer Associate · Free Practice Question Medium
Question 18
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)
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A
Provision fixed resources for each model to avoid unexpected costs, ensuring that the infrastructure is always available for each model
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B
Use a monolithic architecture to manage all machine learning models in a single environment, simplifying management and reducing overhead
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C
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
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D
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
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E
Store all model artifacts and data in Amazon CodeCommit for version control and managing changes over time
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F
Implement a microservices-based architecture with Amazon SageMaker endpoints, where each model is deployed independently, allowing for isolated scaling and updates
Reveal correct answers
Correct answers: C, D, F
Explanation
Correct options:
Implement a microservices-based architecture with Amazon SageMaker endpoints, where each model is deployed independently, allowing for isolated scaling and updates
A microservices-based architecture with Amazon SageMaker endpoints allows each model to be deployed, managed, and scaled independently. This approach enhances maintainability by isolating different components, making it easier to update models or scale specific services without affecting others. It also supports a more scalable and flexible infrastructure.
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
Utilizing infrastructure as code (IaC) with AWS CloudFormation enables you to automate the deployment and management of your ML infrastructure. This approach ensures consistency across environments, simplifies scaling, and allows for rapid deployment in multiple regions. IaC also enhances maintainability by providing a version-controlled, repeatable process for managing infrastructure changes.
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
Storing model artifacts and data in Amazon S3 with versioning is a good practice for maintaining model history and enabling rollbacks.
Incorrect options:
Use a monolithic architecture to manage all machine learning models in a single environment, simplifying management and reducing overhead - A monolithic architecture can simplify management in the short term but becomes difficult to maintain and scale as the number of models and services grows. It also limits flexibility in updating or scaling individual models, leading to potential bottlenecks and higher costs.
Provision fixed resources for each model to avoid unexpected costs, ensuring that the infrastructure is always available for each model - Provisioning fixed resources for each model may lead to underutilization or overprovisioning, resulting in higher costs. Dynamic resource allocation, such as using auto-scaling or spot instances, is generally more cost-effective and scalable.
Store all model artifacts and data in Amazon CodeCommit for version control and managing changes over time - Amazon CodeCommit is the right fit for code-specific version control. You should not use CodeCommit to store model related data.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cloudformation-overview.html
Discussion
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