You work as a machine learning specialist for a farming corporation that wants to use in-ground soil sensors together with enrichment from geolocation, rainfall, and other weather information for the growing area to help identify crop growth stages. They want to use the crop growth information to increase yield and produce more product year over year. They also hope to increase the crop quality through this effort.
The machine learning models that you build for this solution will analyze various growing conditions, such as temperature and humidity. So the farming corporation can schedule watering appropriately for the area.
What collection of AWS services would you use to implement a solution that first trains your model, then gathers the information from the in-ground sensors, then enriches the sensor data, and finally deploys the model to run inference on connected devices in the field?

Answer options:

A.SageMaker, IoT Core, IoT Analytics, IoT Greengrass
B.SageMaker, IoT Core, Kinesis Data Analytics, IoT Greengrass
C.SageMaker, IoT Code, Kinesis Data Streams, IoT Greengrass
D.SageMaker, IoT Core, IoT Analytics, Inference Pipeline

Answer: A
Option A is correct. SageMaker is used to create your model and train it initially. IoT Core sends the sensor data to IoT Analytics for enrichment and analysis. The pre-trained model is deployed into the field using IoT Greengrass so you can perform ML inference using the enriched data on the farm local devices in the field.
Option B is incorrect. You could use Kinesis Data Analytics to analyze your IoT device data streams. Still, IoT Analytics is built specifically for analyzing highly unstructured IoT data. So, it is a better choice.
Option C is incorrect.You could use Kinesis Data Streams to stream your IoT device data, but you would have to write lambda functions to perform the enrichment step. IoT Analytics is built specifically for analyzing and enriching highly unstructured IoT data, so it is a better choice.
Option D is incorrect. Inference Pipeline is used to define and deploy pretrained SageMaker algorithms. Inference Pipeline does not have the IoT inference integration that IoY Greengrass has. So, IoT Greengrass is a better choice for this problem.
Reference:
Please see the AWS IoT Greengrass ML Inference overview, the AWS IoT Analytics overview, the Amazon Kinesis Data Analytics overview, and the Amazon SageMaker developer guide titled Deploy an Inference Pipeline.