Exam AWS Certified Solutions Architect - Professional SAP-C02 topic 1 question 241 discussion

Option B is correct. Feeltwise Option C requires edge agent to collect the data --> Higher operational overhead to migrate as this will need changes in customer application customer has today.

The confusion seem to be b/w IoTCore and FleetWise (B & C), however for anomaly detection one uses Kinesis Data Analytics(KDA) and other uses Glue ML algorithms. Least overhead is using Random Cut Forest in (KDA) as compared to Glue

Option C: How To https://aws.amazon.com/blogs/iot/best-practices-for-ingesting-data-from-devices-using-aws-iot-core-and-or-amazon-kinesis/

ans is C

Answer is D. AWS Lookout - Automatically detect anomalies within metrics and identify their root causes. https://aws.amazon.com/lookout-for-metrics/

Agree with @duriselvan - Fleetwise is made for this and Glue has machine learning modules

C ans :- AWS IoT FleetWise: This managed service simplifies vehicle data collection and management, reducing operational overhead compared to other options. Kinesis data stream: This serverless stream allows processing data in real-time, eliminating the need for custom code. Kinesis Data Firehose: This service automatically stores data in S3, reducing manual intervention. Glue machine learning transforms: These built-in features enable anomaly detection directly within Glue, eliminating the need for separate ML models and infrastructure

Answer B. Straightforward C might sound like a good option with Fleetwise, but Glue for anamoly detection?? Also talks about Kinesis integration with Fleetwise not sure. Fleetwise also needs a Edge agent to communicate with AWS IoT Fleetwise

its a B...oye! :)

Here's why option B is the best choice: Simplicity: This solution leverages AWS IoT Core and Amazon Kinesis Data Firehose, which are fully managed services, making it a simple and low-overhead option. Real-time Data Streaming: AWS IoT Core efficiently receives the vehicle data using the MQTT protocol, and Kinesis Data Firehose streams the data to Amazon S3. This supports data streaming in real-time. Easy Anomaly Detection: Amazon Kinesis Data Analytics can easily be set up to process the streaming data in real-time to detect anomalies. Scalability: This architecture is designed to handle a growing number of vehicles and high data volumes, ensuring scalability without operational overhead. Data Storage: Data is reliably stored in Amazon S3, eliminating concerns about on-premises storage limitations.

I agree with everyone. Even olabiba agrees. It's B.

it's a B C - there is no Fleetwise to Kinesis integration

A - too complex B - It's B. You se IoT Code, Kinesis Firehose and Kinesis Data Analytics for anomalies https://docs.aws.amazon.com/kinesisanalytics/latest/dev/app-anomaly-detection.html C - IoT FleetWise is a perfect use case but this solution does not detect anomalies. You need Lookout for this as described here. https://docs.aws.amazon.com/kinesisanalytics/latest/dev/app-anomaly-detection.html D - This is also possible, but the use case for RabbitMQ is different.

c-c-c-c-c-c-c

B for me opinion i need use Amazon Kinesis Data Analytics for detect anomalies C sounds goood but i don't know how AWS Glue detect anomalies , usually use case is ETL

Olabiba says 'B'.

AWS IoT Core provides a good way to handle data from IoT devices like these smart vehicles, especially as the MQTT protocol is used. Amazon Kinesis Data Firehose can capture, transform, and load streaming data into data lakes, data stores, and analytics services. It can handle large volumes of data from hundreds of thousands of sources, and it can scale automatically. Amazon Kinesis Data Analytics makes it easy to analyze streaming data in real-time with Java, SQL, or Apache Flink, without having to learn new programming languages or processing frameworks. It could be used to analyze the streaming data and detect anomalies