Exam AWS Certified Solutions Architect - Professional SAP-C02 topic 1 question 122 discussion

IOT Core communication supports protocols MQTT, HTTPS, MQTT over WSS, and LoRaWAN (but not FTP/SFTP ) so C should be wrong. Rules Engine: AWS IoT Core provides a rules engine that allows users to define and execute business logic on the data generated by their IoT devices. This enables users to automate actions such as sending notifications, triggering alarms, or updating device settings based on real-time data. Integration with other AWS Services: AWS IoT Core integrates with other AWS services such as AWS Lambda, AWS Kinesis, and AWS S3, allowing users to easily process and store their IoT data, as well as build complex IoT applications using a range of AWS services.

A. Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon S3. Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis. This solution meets the requirement of faster analysis and cost optimization by using AWS IoT Core to collect data from the IoT sensors in real-time and then using AWS Glue and Amazon Athena for efficient data analysis. Option B and D do not optimize the cost of data analysis as they involve use of expensive services like AWS Fargate and Snowball Edge respectively. Option C does not make use of real-time data collection and may not be optimal for faster analysis.

Answer is A — this is the most efficient and scalable solution. Why? AWS IoT Core connects IoT sensors securely and in real-time. An IoT Rule sends data to a Lambda function to parse vendor-specific formats. Parsed data is saved in S3, cataloged by Glue, queried with Athena, and visualized in QuickSight. It's serverless, real-time, and cost-effective, unlike the batch-processing legacy system.

A. Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon. S3 Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis.

A, IoT Core

Option A is best(fastest) and most cost effective.

Everytime the exam shows IOT sensor, think of IOT Core and aws glue

How can A satisfy this requirement? "relational database for analysis" The only option is B with relational database for analysis.

Option A: AWS IoT Core + Lambda Speed: Near real-time data collection and analysis. Flexibility: Ability to adapt to different data formats from multiple vendors. Option C: AWS Transfer for SFTP Speed: There may be network delays and waiting for all data to be sent. Development needs: The sensor code needs to be updated, which increases the development workload. All things considered, option A is better than option C in terms of speed and flexibility, and is especially suitable for real-time or near-real-time requirements.

A for sure

I go for A on the elimination principle although neither of the answers does not seem to fully cover the requirements. I am not sure what is the "vendors' proprietary formats" and not sure why they assume it's csv. Also there is a requirement to load the data in relational database which excludes B. For A we need to assume that S3 covers this requirement.

A is correct, even though it's not clear from the question if the sensors protocol is MQTT or HTTPS. but i can't find other suitable answer so i guess A is the correct one.

Connect the IoT sensors to AWS IoT Core.

A by Elimination rule

I m not convinced about A. It kind of requires changes in the sensors to be compatible with AWS IoT Core.

i'll go for A

A is correct. B: it is appliance, impossible to install on Fargate C: device not use FTP protocol D: snowball is not real time