Explanation
Scenario: The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Timeline Description automatically generated

Reference:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/stream-analytics/stream-analytics-window-fun

Reference:
https://azure.microsoft.com/en-gb/blog/announcing-azure-monitor-aiops-alerts-with-dynamic-thresholds/

Explanation
Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics support dynamic data masking. Dynamic data masking limits sensitive data exposure by masking it to non-privileged users.
The Credit card masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.
Example:
XXXX-XXXX-XXXX-1234

Explanation
A screenshot of a computer Description automatically generated with low confidence

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/purchasing-models
https://docs.microsoft.com/en-us/azure/azure-sql/database/single-database-create-arm-template-quickstart

Explanation
The lifecycle management policy lets you:
Delete blobs, blob versions, and blob snapshots at the end of their lifecycles Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts