Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 6 discussion

Real-Time Inference: Immediate responses for high-traffic, low-latency applications. >> Asynchronous Inference: Near real-time for large payloads and longer processing. Batch Transform: Large-scale, offline processing without real-time needs. Serverless Inference: Low-latency inference for intermittent or unpredictable traffic without managing infrastructure.

C: Asynchronous inference Explanation: Asynchronous inference in Amazon SageMaker is specifically designed to handle large payloads (up to 1 GB) and long processing times (up to 1 hour). It decouples request submission from processing, allowing the client to submit a request and receive a response later when the inference is complete. This makes it suitable for use cases where real-time responses are not strictly required, but near real-time results are needed.

Whenever "near real-time latency" - asynchronous inference

C is right. Amazon SageMaker Asynchronous Inference is a capability in SageMaker that queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up to one hour), and near real-time latency requirements. Asynchronous Inference enables you to save on costs by autoscaling the instance count to zero when there are no requests to process, so you only pay when your endpoint is processing requests.

I think answer A is the correct one as the customer wants to have real-time inference, right?

Key word "real-time latency"

Asynchronous inference PDF RSS Amazon SageMaker Asynchronous Inference is a capability in SageMaker that queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up to one hour), and near real-time latency requirements. Asynchronous Inference enables you to save on costs by autoscaling the instance count to zero when there are no requests to process, so you only pay when your endpoint is processing requests.

Amazon SageMaker Asynchronous Inference would be the appropriate option. Here’s why: • Handles Large Payloads: Asynchronous Inference is designed to handle large input payloads (up to several GBs) that are typically not suited for real-time, low-latency processing. • Long Processing Times: It supports inference requests that can take minutes to hours to complete, making it ideal for models that require significant processing time. • Near Real-Time Response: While it does not provide millisecond-level latency like real-time endpoints, it offers a more scalable and efficient solution for near real-time use cases where the response time can range from seconds to minutes.