Q30 — AWS SAP-C02 Ch.2

Question 30 of 75 | ← Chapter 2

Q180. A manufacturing company is building an inspection solution for its factory. The company has IP cameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images. The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factory's internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers. How should the company deploy the ML model to meet these requirements?

• A. Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected
• B. Deploy AWS loT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected ✓
• C. Order an AWS Snowball device. Deploy a SageMaker endpoint, the ML model, and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected
• D. Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected

Correct Answer: B. Deploy AWS loT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected

Explanation

This solution will allow the company to deploy the ML model locally on the Linux server using AWS IoT Greengrass, which can function even if the internet connectivity is down. The Greengrass component can be used to take still images from the IP cameras and run inference using the deployed ML model. When a defect is detected, the component can be configured to call the local API hosted on the same server to provide feedback to the factory workers. This solution can also be easily scalable to accommodate additional cameras or changes in the deployment architecture. this is taking about detecting defects from an image that is taken from a camera. I would go for running a ML model on IoT greengras pc and transfer it to IoT core, then store it in s3 bucket, which can be called by api function via lambda to send it to users. option D would monitor only sensor data of machines.