AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 47

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?

Answer options

• A. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
• B. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
• C. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
• D. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

Correct answer: A

Explanation

The correct answer is A because SageMaker real-time inference provides low-latency predictions, which addresses the delay issue reported by consumers. Additionally, SageMaker Model Monitor can effectively notify the team about any deviations in model quality. The other options either do not provide real-time inference or use methods that do not meet both requirements effectively.