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Question 504

A company is migrating its on-premises IoT platform to AWS. The platform consists of the following components:

• A MongoDB cluster as a data store for all collected and processed IoT data.

• An application that uses Message Queuing Telemetry Transport (MQTT) to connect to IoT devices every 5 minutes to collect data.

• An application that runs jobs periodically to generate reports from the IoT data. The jobs take 120-600 seconds to finish running.

• A web application that runs on a web server. End users use the web application to generate reports that are accessible to the general public.

The company needs to migrate the platform to AWS to reduce operational overhead while maintaining performance.

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.

Correct Answer: A, D, E

To meet the requirements with the least operational overhead, the following steps are appropriate: Use AWS Step Functions with AWS Lambda to prepare and store reports in Amazon S3 and configure Amazon CloudFront to serve these reports. This combination efficiently handles the periodic job requirements while minimizing management efforts. Connect IoT devices to AWS IoT Core, which facilitates MQTT messaging and can trigger AWS Lambda functions to process and store data, providing an efficient and scalable solution for handling IoT data. Migrate the MongoDB cluster to Amazon DocumentDB (with MongoDB compatibility) to eliminate the operational overhead of managing MongoDB, while maintaining the same functionalities and performance required for the IoT data store.

Discussion

awsazOptions: ADE

Step Functions and Lambda for Report Generation (A): AWS Step Functions and Lambda can manage the periodic jobs to generate reports with minimal operational overhead. By using Amazon S3 for storage and Amazon CloudFront for distribution, the solution provides scalability and reliability with minimal management. AWS IoT Core for MQTT Messaging (D): AWS IoT Core is a managed service that simplifies the connection and management of IoT devices. Using IoT rules and Lambda functions ensures efficient message processing and data storage with minimal overhead. Amazon DocumentDB for MongoDB Compatibility (E): Amazon DocumentDB is a managed database service compatible with MongoDB, which reduces the operational burden of managing a MongoDB cluster while maintaining performance and scalability.

vip2Options: ADE

A, D and E meet all requirements clearly

mifuneOptions: ADE

A - for preparing the reports and writing them to Amazon S3 | D - for handling connections from IoT devices | E - supporting MongoDB workloads.