A team is training a large language model that requires high memory bandwidth and parallel processing. Which compute environment is most appropriate for this training job?

Memory-optimized instances without GPU

CPU instances with high clock speed

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 5)

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 21–25

21foundation · easy
An e-commerce website needs to provide product recommendations to users instantly as they browse. Which inference method should be used to meet this low-latency requirement?
Select an answer first
22foundation · easy
A team is training a large language model that requires high memory bandwidth and parallel processing. Which compute environment is most appropriate for this training job?
Select an answer first
23foundation · easy
A team needs to orchestrate a complex ML workflow that includes data preprocessing, training, and deployment steps, with dependencies between them. Which AWS service is purpose-built for this?
Select an answer first
24foundation · easy
A mobile app needs to provide instant fraud detection for each transaction. Which deployment strategy is most appropriate?
Select an answer first
25foundation · easy
A team needs to deploy a model that processes large payloads (e.g., 100 MB) and can tolerate a few minutes of latency. Which SageMaker endpoint type is designed for this use case?
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 5)

Questions 21–25

An e-commerce website needs to provide product recommendations to users instantly as they browse. Which inference method should be used to meet this low-latency requirement?

A team is training a large language model that requires high memory bandwidth and parallel processing. Which compute environment is most appropriate for this training job?

A team needs to orchestrate a complex ML workflow that includes data preprocessing, training, and deployment steps, with dependencies between them. Which AWS service is purpose-built for this?

A mobile app needs to provide instant fraud detection for each transaction. Which deployment strategy is most appropriate?

A team needs to deploy a model that processes large payloads (e.g., 100 MB) and can tolerate a few minutes of latency. Which SageMaker endpoint type is designed for this use case?

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