A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time…

The correct answer is A. Real-time inference. Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial…

Submitted by fatema_kw· Mar 30, 2026

Question

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency. Which SageMaker inference option meets these requirements?

Options

AReal-time inference
BServerless inference
CAsynchronous inference
DBatch transform

How the community answered

(26 responses)

A
81% (21)
B
4% (1)
C
4% (1)
D
12% (3)

Explanation

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing

Topics

#SageMaker inference#Real-time inference#ML deployment#Latency considerations

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