Question 131:
You work for an Internet of Things (IoT) component manufacturer which builds servos, engines, sensors, etc. The IoT devices transmit usage and environment information back to AWS IoT Core via the MQTT protocol. You want to use a machine learning model to show how/where the use of your products is clustered in various regions around the world. This information will help your data scientists build KPI dashboards to improve your component engineering quality and performance. You have created, trained, and deployed to Amazon SageMaker Hosting Services your model based on the XGBoost algorithm. Your model is set up to receive inference requests from a lambda function that is triggered by the receipt of an IoT Core MQTT message via your Kinesis Data Streams instance. What transform steps need to be done for each inference request? Also, which steps are handled by your code versus by the inference algorithm? (Select TWO)
Answer options:
A.Inference request serialization (handled by the algorithm) B.Inference request serialization (handled by your lambda code) C.Inference request deserialization (handled by your lambda code) D.Inference request deserialization (handled by the algorithm) E.Inference request post serialization (handled by the algorithm)