A well-architected serverless mobile application is being analyzed to get more insights into improving the user experience. The business unit chief has gathered data scientists in the analytics and machine learning team to design SQL queries, dashboards and offer visualizations based on logs to transition to a decision-making stage. You are the DevOps engineer assigned to discuss and support the solution architecture. What are some of the important aspects of the log delivery pipeline to implement the architecture proposal?

Answer options:

A.Enable Amazon Kinesis Data Firehose data transformation. When creating the delivery stream, set the DeliveryStreamType property as DirectPut and the AWS CloudWatch Logs SubscriptionFilter destination as the delivery stream ARN. Amazon Kinesis Data Firehose invokes the specified Lambda function asynchronously to transform logs into JSON format records. Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 5 minutes. An AWS Glue database and crawler components discover new transformations from AWS Lambda. Data scientists can run SQL-like queries on top of Amazon Athena and Amazon QuickSight for further analysis.
B.Enable Amazon Kinesis Data Firehose data transformation. When creating the delivery stream, set the DeliveryStreamType property. Amazon Kinesis Data Firehose invokes the specified Lambda function asynchronously to transform logs into JSON format records. Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 30 minutes. Data scientists can run SQL-like queries on top of Amazon Athena and Amazon QuickSight for further analysis.
C.Enable Amazon Kinesis Data Firehose data transformation. When creating the delivery stream, set the DeliveryStreamType property as KinesisStreamAsSource. Amazon Kinesis Data Firehose invokes the specified Lambda function asynchronously to transform logs into JSON format records. Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 5 minutes. Data scientists can run SQL-like queries on top of Amazon Athena and Amazon QuickSight for further analysis.
D.Enable Amazon Kinesis Data Firehose data transformation. When creating the delivery stream, set the DeliveryStreamType property as KinesisStreamAsSource while the AWS CloudWatch Logs SubscriptionFilter destination as the delivery stream ARN. Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 30 minutes. Data scientists can run SQL-like queries on top of Amazon Athena and Amazon QuickSight for further analysis.

Correct Answer: A
The key points in the question are SQL queries for analytics purposes based on logs and implementing a log delivery pipeline. What is common in all answers is the use of Amazon Kinesis Data Firehose data transformation. You can enable Amazon Kinesis Data Firehose data transformation when you create your delivery stream. The DeliveryStreamType property indicates that the delivery stream delivers real-time streaming data to a destination. The delivery stream type has to be set as DirectPut since it allows access to the delivery stream directly or to send records from CloudWatch Logs among other options. Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 5 minutes. If your Lambda function takes more than 5 minutes to complete, you get the following error: Firehose encountered timeout errors when calling AWS Lambda. The maximum supported function timeout is 5 minutes.
Incorrect Answers:
Option B is incorrect because Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 5 minutes, not 30 minutes.
Option C is incorrect because the DeliveryStreamType property as KinesisStreamAsSource is used when the delivery stream uses a Kinesis data stream as a source. This is not the case here.
Option D is incorrect because the DeliveryStreamType property as KinesisStreamAsSource is used when the delivery stream uses a Kinesis data stream as a source. This is not the case here. Besides, Amazon Kinesis Data Firehose supports a Lambda invocation time of up to 5 minutes, not 30 minutes.
References:
https://amzn.to/3fDcsla
https://go.aws/3bkwIog