Which combination of steps will meet these requirements with the LEAST operational overhead?
(Choose three.)
Create AWS Step Functions state machines with AUS Lambda tasks to prepare the reports and to write the reports to Amazon S3. Configure an Amazon CloudFront distribution that has an S3 origin to serve the reports
Create an AWS Lambda function. Program the Lambda function to connect to the IoT devices. process the data, and write the data to the data store. Configure a Lambda layer to temporarily store messages for processing.
Configure an Amazon Elastic Kubernetes Service (Amazon EKS) cluster with Amazon EC2 instances to prepare the reports. Create an ingress controller on the EKS cluster to serve the reports.
Connect the IoT devices to AWS IoT Core to publish messages. Create an AWS IoT rule that runs when a message is received. Configure the rule to call an AWS Lambda function. Program the Lambda function to parse, transform, and store device message data to the data store.
Migrate the MongoDB cluster to Amazon DocumentDB (with MongoDB compatibility).
Migrate the MongoDB cluster to Amazon EC2 instances.
Explanations:
AWS Step Functions can manage the periodic jobs to generate reports. Lambda functions can process the reports, and S3 with CloudFront will minimize operational overhead for serving reports. This architecture is serverless and reduces management complexity.
While Lambda is suitable for many tasks, managing IoT connections through Lambda would be inefficient. IoT device communication and data collection would be better handled by AWS IoT Core, which is designed for this purpose.
Amazon EKS requires managing Kubernetes clusters, which introduces additional operational overhead. It is not the least operationally intensive option for processing reports compared to serverless alternatives like AWS Step Functions and Lambda.
AWS IoT Core is designed to handle MQTT communication, and using an IoT rule to trigger Lambda functions to process and store data is an efficient, serverless solution. It reduces operational overhead by automating the data processing pipeline.
Migrating to Amazon DocumentDB (with MongoDB compatibility) provides a managed service alternative to MongoDB. It reduces operational overhead as AWS handles scaling, backups, and maintenance.
Migrating to EC2 instances would require managing virtual machines, which increases operational overhead. Using a managed service like DocumentDB is more efficient and requires less maintenance.