HikeHills.com (HH) is an online specialty retailer that sells clothing and outdoor refreshment gear for trekking, go camping, boulevard biking, mountain biking, rock hiking, ice mountaineering, skiing, avalanche protection, snowboarding, fly fishing, kayaking, rafting, road and trace running, and many more.
HH runs its entire online infrastructure on java based web applications running on AWS. The HH is capturing clickstream data and uses a custom-build recommendation engine to recommend products that eventually improve sales, understand customer preferences and already using AWS Streaming capabilities to collect events and transaction logs and process the stream.
HH has 2 different Kinesis Streams that are being configured as 2 in-application streams to be accessed by the Kinesis Analytics application. The data joined between the 2 in-application streams need to go through multiple transformations and conversions. What are the key artifacts of streaming SQL capabilities provided by Kinesis analytics? Select 3 options.

Answer options:

A.in-application stream and Kinesis data stream are the same
B.in-application stream and kinesis firehose stream are same
C.More in-application streams are needed to store intermediate query results
D.in-application streams are needed cannot store intermediate query results
E.Insert data into the in-application stream using a pump
F.the in-application stream is analogous to DB SQL paradigm

Answer: C,E,F
Option A is incorrect -In-application streams exist only in the context of an Amazon Kinesis Data Analytics application. Kinesis data streams and Kinesis Data Firehose delivery streams exist independently of your application.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html
Option B is incorrect - In-application streams exist only in the context of an Amazon Kinesis Data Analytics application. Kinesis data streams and Kinesis Data Firehose delivery streams exist independently of your application.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html
Option C is correct -more in-application streams as needed to store intermediate query results
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html
Option D is incorrect -more in-application streams as needed to store intermediate query results
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html
Option E is correct -Insert data into the in-application stream using a pump which is a continuous insert query running that inserts data from one in-application stream to another in-application stream
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html
When you configure application input, you map a streaming source to an in-application stream that is created. Data continuously flows from the streaming source into the in-application stream. An in-application stream works like a table that you can query using SQL statements, but it`s called a stream because it represents continuous data flow.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streams-pumps.html