Which solution will meet these requirements?
Create and deploy a model by using Amazon SageMaker Autopilot. Create a real-time endpoint that the web application invokes when new photos are uploaded.
Create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.
Create an Amazon CloudFront function that uses Amazon Comprehend to detect unwanted content. Associate the function with the web application.
Create an AWS Lambda function that uses Amazon Rekognition Video to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.
Explanations:
Amazon SageMaker Autopilot is used to create and deploy machine learning models, which contradicts the requirement of not using ML models.
Using AWS Lambda with Amazon Rekognition allows for real-time detection of unwanted content in uploaded photos without requiring the training of a machine learning model, meeting the requirement.
Amazon Comprehend is primarily used for natural language processing and is not suitable for detecting unwanted content in images, making this option irrelevant for photo content moderation.
Amazon Rekognition Video is designed for analyzing video content, not still images. Since the requirement is to analyze photos, this option does not meet the needs.
I arrange that the answer is:
Create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.
I rate that the answer is:
Create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.