A company is migrating its on‑premises IoT platform to AWS. The platform consists of: a MongoDB cluster storing collected and processed IoT data; an app that uses MQTT to poll devices every 5 minutes; periodic jobs that generate reports (jobs run 120–600 seconds); and a public web application that serves reports. The company wants to reduce operational overhead while maintaining performance. Which combination of steps provides the required solution with the LEAST operational overhead? (Choose three.)

Migrating MongoDB to Amazon DocumentDB reduces operational overhead by providing a fully managed, scalable, and highly available NoSQL database compatible with MongoDB. AWS IoT Core with an IoT rule triggering a Lambda function offers a serverless and scalable solution for ingesting and processing device data, eliminating the need for self-managed MQTT polling applications. Using AWS Step Functions with Lambda for report generation and Amazon S3 with CloudFront for serving reports provides a serverless, cost-effective, and highly scalable architecture for periodic jobs and web content delivery. Deploying EKS on EC2 instances would increase operational overhead compared to serverless options. Migrating MongoDB to EC2 instances would retain the operational burden of self-managing the database. A Lambda function directly connecting to IoT devices is not the standard or most efficient pattern for IoT data ingestion; AWS IoT Core is designed for this purpose.