Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stockbroking businesses as well as unit trust and asset administration, having served the financial community over the past five decades.
AFS has built its entire infrastructure on AWS which includes web applications built on EC2, Files and logs on S3, databases on Amazon RDS, DynamoDB and DWH on Redshift. AFS is defining data sources. Please help identify the tasks.
Select 3 options.

Answer options:

A.Data of files in S3, tables, views, and collections in databases are the data sources.
B.Amazon ML data sources can be created only for RDS, Redshift, and S3.
C.Amazon ML provides only 2 options to split the datasets, sequential and random split.
D.Metadata of files in S3, tables, views, and collections in databases are the data sources.
E.Amazon ML data sources can be created on any of the above data sources.
F.AttributeType includes Binary, Categorical, Numeric and Text datatypes.
G.AttributeType includes Ranking, Categorization, Word Prominence, Count number, and Count percentage.

Answer: B, D, F
Option A is incorrect -This is called schema which is composed of all attributes in the input data and their corresponding data types
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-a-data-schema-for-amazon-ml.html
Option B is correct -Data Sources can be created only for RDS, Redshift, and S3.
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-and-using-datasources.html
Option C is incorrect -Amazon ML provides three options for splitting your data:
Pre-split the data - Split the data into two data input locations, before uploading them to Amazon Simple Storage Service (Amazon S3) and creating two separate data sources with them.
Amazon ML sequential split – configure Amazon ML to split your data sequentially when creating the training and evaluation data sources.
Amazon ML random split - Configure Amazon ML to split your data using a seeded random method when creating the training and evaluation data sources.
Option D is correct -Data source objects contain metadata about your input data. for a data source, Amazon ML reads your input data, computes descriptive statistics on its attributes, and stores the statistics, a schema, and other information as part of the data source object
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-and-using-datasources.html
Option E is incorrect - Data Source cannot be created on DynamoDB 
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-and-using-datasources.html
Option F is correct -AttributeType includes Binary, Categorical, Numeric and Text datatypes
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-a-data-schema-for-amazon-ml.html#assigning-data-types
Option G is incorrect -these define the distribution of text attributes. https://docs.aws.amazon.com/machine-learning/latest/dg/data-insights.html