A team has a containerized ML model that runs on an on-premises Kubernetes cluster. They want to migrate to AWS with minimal changes to their existing container orchestration and deployment practices. The model is already packaged as a Docker image with a custom inference server. Which deployment target is most appropriate?

SageMaker real-time endpoint with a custom container

Amazon ECS with the existing Docker image

Amazon EKS with the existing Docker image

AWS Lambda with a container image

A team has a trained model that they want to deploy to a variety of edge devices, including different CPU architectures (ARM and x86) and different frameworks (TensorFlow and PyTorch). They want to optimize the model for each device without manually tuning each one. Which approach should they use?

Deploy the model to a SageMaker endpoint and have the edge devices call it

Use SageMaker batch transform to generate optimized models

Manually convert the model to each framework and optimize by hand

Use SageMaker Neo to compile the model for each target hardware

A team wants to track different versions of a model artifact and the associated metadata, such as training dataset and hyperparameters, so they can reproduce a specific model deployment. Which AWS service is designed for this purpose?

Amazon SageMaker Model Registry

AWSCertified Machine Learning Engineer - Associate

Domain 3Objective 1

Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 1)

Part of the Content Domain 3: Deployment and Orchestration of ML Workflows domain, which accounts for 22% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~11–19 in this domain), expect 4–6 from this objective — we provide 33 practice questions to prepare you well beyond it. (estimate)

33questions here

7free pages

13concepts

22%of the exam

13 Concepts in This Objective

Deployment best practices
AWS deployment services
Real-time and batch inference methods
Compute provisioning for production and test
Endpoint types and requirements
Container selection
Edge optimization with SageMaker Neo
Performance, cost, and latency tradeoffs
Compute environment selection for training and inference
Deployment orchestrators
Multi-model and multi-container deployments
Deployment targets
Model deployment strategies

Questions 1–5

1application · medium
A team has a containerized ML model that runs on an on-premises Kubernetes cluster. They want to migrate to AWS with minimal changes to their existing container orchestration and deployment practices. The model is already packaged as a Docker image with a custom inference server. Which deployment target is most appropriate?
Select an answer first
2expert · hard
A team has a trained model that they want to deploy to a variety of edge devices, including different CPU architectures (ARM and x86) and different frameworks (TensorFlow and PyTorch). They want to optimize the model for each device without manually tuning each one. Which approach should they use?
Select an answer first
3foundation · easy
A team wants to track different versions of a model artifact and the associated metadata, such as training dataset and hyperparameters, so they can reproduce a specific model deployment. Which AWS service is designed for this purpose?
Select an answer first
4foundation · easy
A team has several small models that are used infrequently and wants to reduce hosting costs by sharing a single endpoint. Which deployment strategy is most appropriate?
Select an answer first
5foundation · easy
A team needs to deploy a model that requires two different containers: one for preprocessing and one for inference. Which SageMaker deployment strategy supports this requirement?
Select an answer first

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Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 1)

Questions 1–5

A team has a trained model that they want to deploy to a variety of edge devices, including different CPU architectures (ARM and x86) and different frameworks (TensorFlow and PyTorch). They want to optimize the model for each device without manually tuning each one. Which approach should they use?

A team wants to track different versions of a model artifact and the associated metadata, such as training dataset and hyperparameters, so they can reproduce a specific model deployment. Which AWS service is designed for this purpose?

A team has several small models that are used infrequently and wants to reduce hosting costs by sharing a single endpoint. Which deployment strategy is most appropriate?

A team needs to deploy a model that requires two different containers: one for preprocessing and one for inference. Which SageMaker deployment strategy supports this requirement?

Keep studying — Content Domain 3: Deployment and Orchestration of ML Workflows