Exam AWS Certified Solutions Architect - Professional SAP-C02 topic 1 question 526 discussion

https://aws.amazon.com/blogs/iot/ingesting-enriched-iot-data-into-amazon-s3-using-amazon-kinesis-data-firehose/

In this application, sensor data is transmitted at the following intervals: 1. Device to IoT Core (every 5 seconds) 2. IoT Core to S3 (every 30 minutes) The data load from IoT Core to S3 doesn't necessarily need to be real-time, and the most cost-effective solution is option A. Option A uses the simplest method to load data without using resources like Kinesis.

Option B stands out as the most cost-effective and efficient solution. Kinesis Data Firehose is specifically designed for this type of workload: collecting streaming data, optionally transforming it with Lambda, and delivering it to S3 in optimized batches. The 900-second buffering interval ensures that the data is available in S3 well within the 30-minute requirement while minimizing S3 PUT requests and optimizing Lambda invocation costs for enrichment. AWS IoT Core Basic Ingest further contributes to cost savings by providing a leaner ingestion path.

the requirements is MOST cost-effective solutions. hence, I choosed A. (i.e. Less resources)

B is right, this meets the requirement of making new sensor data available in Amazon S3 less than 30 minutes after the application collects the data. The buffering interval of 900 seconds (15 minutes) is sufficient to meet this requirement, and the use of Kinesis Data Firehose ensures that the data is processed and delivered to Amazon S3 in a timely manner. This solution is also cost-effective because it: Uses AWS IoT Core Basic Ingest, which is free for up to 10 GB of incoming data per month. Uses Kinesis Data Firehose, which has a low cost compared to other services like Lambda or Timestream. Does not require the creation of multiple resources (e.g., Lambda functions, topics) as in some other solutions.

Option A is the most cost-effective solution because: * Uses minimal services while meeting all requirements * Leverages serverless architecture for automatic scaling * Provides immediate processing without buffering delays * Minimizes costs by eliminating unnecessary services * Direct integration between IoT Core and Lambda ensures low latency The Lambda function can process messages immediately as they arrive from IoT Core, enrich the data, and write to S3 well within the 30-minute requirement. This architecture is both simple and cost-effective, avoiding unnecessary services and their associated costs.

B. buffering helps with cost of lambda

AWS IoT Core Basic is cheaper than AWS IoT core, also Kinesis Data Firehose Batching will reduce the number of write operations to S3 and Lambda invocations by buffering data. Hence even though there is an additional component of Kinesis Data Firehose, it is more cost effective than option A. According to me, the answer is Option B

No other applications are processing the sensor data from AWS IoT Core: Use AWS IoT Core Basic Ingest to ingest the sensor data to reduce messaging cost: B or D https://docs.aws.amazon.com/iot/latest/developerguide/iot-basic-ingest.html Configure an AWS IoT rule action to write the data to Amazon KDF or KDS? "New sensor data must be available in Amazon S3 less than 30 minutes after the application collects the data." =>near real time, stream data to s3, no need storage or replay, we shd use autoscaling and fully managed KDF.

A. The advantage of this method is its simplicity and high real-time performance, as Lambda functions can immediately respond to IoT events. The cost of Lambda functions is based on execution time and resource usage, which is very economical for small data processing tasks. B. The 900 second buffer interval of Kinesis Data Firehose does not meet real-time requirements (data needs to be processed within 30 minutes, while the set buffer here is 15 minutes). In addition, introducing Kinesis Data Firehose adds additional cost and complexity, especially when Lambda functions can directly process data.

Many devices sending data every 5 seconds, it's not necessary, due that you just need the data available in S3 within 30 minutes!

the data emitting time is 5 sec and lambda may take upto 15 mins to enrich the data , this detail is only captured in buffer section of KFS , hence going with B, If there is SQS queue in option A before lambda then i would have choosen that

As per this link it is B, firehose is used: https://aws.amazon.com/blogs/iot/ingesting-enriched-iot-data-into-amazon-s3-using-amazon-kinesis-data-firehose/

We need simple and cost effective so choosing A

I prefer B

Best Answer: B. Use AWS IoT Core Basic Ingest to ingest the sensor data. Configure an AWS IoT rule action to write the data to Amazon Kinesis Data Firehose. Set the Kinesis Data Firehose buffering interval to 900 seconds. Use Kinesis Data Firehose to invoke an AWS Lambda function to enrich the data, Configure Kinesis Data Firehose to deliver the data to Amazon S3. Reasoning: Cost-effective: IoT Core Basic Ingest is the most cost-effective option for high-volume, low-value data. Low latency: Kinesis Data Firehose with a 900-second buffering interval provides a balance between cost and latency, meeting the requirement of data availability in S3 within 30 minutes. Scalability: Kinesis Data Firehose can handle high throughput, making it suitable for large volumes of sensor data. Simplicity: The solution involves a straightforward pipeline with minimal components.

B is correct