You are working for a large supermarket chain as a DevOps engineer. Your company has stored a huge amount of data in different AWS and on-premise sources. The data includes daily transactions, customer data, etc. You have a task to build up an AWS QuickSight service that can use machine learning technologies to help other teams to analyze data, visualize data through dashboards and discover hidden trends. Which data can NOT be provided to QuickSight for analysis?

Answer options:

A.An AWS Redshift cluster.
B.A MySQL 5.1 database in the customer’s data center which is internet accessible.
C.A MariaDB RDS instance.
D.YAML files stored in S3 buckets.

Correct Answer – D
AWS QuickSight can use various relational data stores as data sources to create an analysis via machine learning technologies.
Please check https://docs.aws.amazon.com/quicksight/latest/user/supported-data-sources.html for the details of data sources. One more thing is that the question asks for the option which contains an invalid data source type.
Option A is incorrect: Because Amazon Redshift is based on industry-standard PostgreSQL and is supported by AWS QuickSight.
Option B is incorrect: Because MySQL database is supported in an on-premises network as long as it can be accessible from Amazon QuickSight.
Option C is incorrect: Because AWS RDS is a valid data source type.
Option D is CORRECT: Because the valid data types include:
CSV/TSV – Delimited text files
ELF/CLF – Extended and common log format files
JSON – Flat or semi-structured data files
XLSX – Microsoft Excel files
Although S3 is valid, YAML is not supported. Therefore this option should be chosen.