A company wants to build a data lake which will comprise of the following
Logs generated from CloudTrail
VPC Flow Logs
Logs from Application Load Balancers hosted in AWS
They need to stream the logs and also ensure an expansive data lake is available for storage purposes. Which of the following can be used to fulfil this requirement in the most efficient manner? Choose 2 answers from the options given below

Answer options:

A.Use AWS S3 for storage of the data lake
B.Use AWS Kinesis Streams for storage of the data lake
C.Use AWS Kinesis Firehose to stream the various files
D.Use AWS Kinesis streams

Answer – A and C
The AWS Documentation mentions the following
Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.
Option B is incorrect since this option should not be used for persistence of data
Option D is incorrect since this would be less appropriate than Kinesis Firehose. Kinesis Firehose can automatically take the ingestion of data from multiple sources and then directly ingest the data into S3.
For more information on a use case for this, please refer to the below URL
https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/