A company has a model that is used for both real-time fraud detection (latency < 100 ms) and monthly batch risk scoring (millions of records). They currently have two separate deployments: a real-time endpoint and a batch transform job. The team wants to reduce infrastructure cost without sacrificing the real-time SLO. Which approach is most appropriate?

Use a single asynchronous endpoint for both real-time and batch workloads

Use a single real-time endpoint for both real-time and batch workloads

Keep the real-time endpoint and batch transform job as separate deployments

Use a real-time endpoint for real-time and a serverless endpoint for batch

A company needs to deploy a model with strict latency requirements (under 100 ms) for a customer-facing application. Which deployment option is most likely to meet this requirement?

Real-time endpoint with GPU instances

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 3)

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 11–15

11expert · hard
A company has a model that is used for both real-time fraud detection (latency < 100 ms) and monthly batch risk scoring (millions of records). They currently have two separate deployments: a real-time endpoint and a batch transform job. The team wants to reduce infrastructure cost without sacrificing the real-time SLO. Which approach is most appropriate?
Select an answer first
12foundation · easy
A team is choosing between a real-time endpoint and a batch transform job for a model that processes data once a day. Which tradeoff is most relevant in this decision?
Select an answer first
13foundation · easy
A company needs to deploy a model with strict latency requirements (under 100 ms) for a customer-facing application. Which deployment option is most likely to meet this requirement?
Select an answer first
14foundation · easy
A team already runs their applications on Amazon EKS and wants to deploy their ML models using the same infrastructure and tooling. Which deployment target is most appropriate?
Select an answer first
15foundation · easy
A team is deploying a model for inference that is not compute-intensive but requires low latency. Which compute environment is most cost-effective for this inference workload?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to MLA-C01

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by AWS. “MLA-C01” is a trademark of its owner, used for identification only.

Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 3)

Questions 11–15

A team is choosing between a real-time endpoint and a batch transform job for a model that processes data once a day. Which tradeoff is most relevant in this decision?

A company needs to deploy a model with strict latency requirements (under 100 ms) for a customer-facing application. Which deployment option is most likely to meet this requirement?

A team already runs their applications on Amazon EKS and wants to deploy their ML models using the same infrastructure and tooling. Which deployment target is most appropriate?

A team is deploying a model for inference that is not compute-intensive but requires low latency. Which compute environment is most cost-effective for this inference workload?

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