Clickstream data is captured through Streams API, uses Kinesis Data Streams for streaming, and loads the data into Kinesis Analytics for further processing before being loaded into Redshift. The team want to perform some pre-processing of data using lambda functions before Kinesis Analytics application SQL code executes.
Which of the features mentioned below are supported in lambda functions for pre-processing of data? Select 3 options

Answer options:

A.Data Enrichment
B.Complex string transformation
C.Data Filtering
D.Data Transformation into Avro and Parquet formats
E.Record Format Conversion

Answer : A,B and C
Option A is correct - The data in your stream needs format conversion, transformation, enrichment, or filtering, you can preprocess the data using an AWS Lambda function. You can do this before your application SQL code executes or before your application creates a schema from your data stream.
Using a Lambda function for pre-processing records is useful in the following scenarios:
Transforming records from other formats (such as KPL or GZIP) into formats that Kinesis Data Analytics can analyze. Kinesis Data Analytics currently supports JSON or CSV data formats.
Expanding data into a format that is more accessible for operations such as aggregation or anomaly detection. For instance, if several data values are stored together in a string, you can expand the data into separate columns.
Data enrichment with other AWS services, such as extrapolation or error correction.
Applying complex string transformation to record fields.
Data filtering for cleaning up the data.
Option E mentions about parquet and avro format conversion, which is not supported as part of preprocessing
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/lambda-preprocessing.html