A company runs a SageMaker AI training job using a custom container. They want to use Spot Instances to reduce costs, but the training job is not fault-tolerant and cannot be interrupted. Which approach should they take?

Use on-demand instances for the training job

Use a mix of on-demand and Spot Instances

Use Spot Instances with a capacity rebalance recommendation

Use Spot Instances with a checkpointing mechanism

A large enterprise has a complex ML infrastructure with multiple teams managing different parts. They need to enforce consistent security policies and compliance controls across all infrastructure. The teams have varying levels of expertise in YAML and Python. Which IaC approach best balances governance and developer productivity?

Use AWS SAM for all ML infrastructure

Use AWS CDK with custom constructs that encapsulate security policies

Use Terraform with a custom module registry

Use AWS CloudFormation with a centralized template repository and strict review processes

What is the purpose of the SageMaker SDK's delete_endpoint() method?

To remove a deployed endpoint and its associated resources

To list all endpoints in the account

To update the endpoint configuration

AWSCertified Machine Learning Engineer - Associate

Domain 3Objective 2

Task 3.2: Create and Script Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 4)

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

11concepts

22%of the exam

11 Concepts in This Objective

On-demand vs provisioned resources
Scaling policy comparison
Infrastructure as code options
Containerization fundamentals
SageMaker endpoint auto scaling
Best practices for ML solutions
Automating compute provisioning
Building and maintaining containers
SageMaker endpoints in VPC
Deploying models with SageMaker SDK
Auto scaling metrics selection

Questions 16–20

16foundation · easy
A workload experiences unpredictable traffic that varies based on user activity. Which scaling policy is most appropriate?
Select an answer first
17expert · hard
A company runs a SageMaker AI training job using a custom container. They want to use Spot Instances to reduce costs, but the training job is not fault-tolerant and cannot be interrupted. Which approach should they take?
Select an answer first
18expert · hard
A large enterprise has a complex ML infrastructure with multiple teams managing different parts. They need to enforce consistent security policies and compliance controls across all infrastructure. The teams have varying levels of expertise in YAML and Python. Which IaC approach best balances governance and developer productivity?
Select an answer first
19foundation · easy
What is the purpose of the SageMaker SDK's delete_endpoint() method?
Select an answer first
20foundation · easy
A team is running a training job that must start immediately and run for only 2 hours. The job is not part of a recurring workload. Which compute purchasing option is most appropriate?
Select an answer first

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Task 3.2: Create and Script Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 4)

Questions 16–20

A workload experiences unpredictable traffic that varies based on user activity. Which scaling policy is most appropriate?

A company runs a SageMaker AI training job using a custom container. They want to use Spot Instances to reduce costs, but the training job is not fault-tolerant and cannot be interrupted. Which approach should they take?

What is the purpose of the SageMaker SDK's delete_endpoint() method?

A team is running a training job that must start immediately and run for only 2 hours. The job is not part of a recurring workload. Which compute purchasing option is most appropriate?

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