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Answer: Implement a Databricks notebook that uses the VERSION AS OF syntax to snapshot the dataset at weekly intervals, comparing consecutive snapshots for changes.
Implementing a Databricks notebook with the VERSION AS OF syntax allows for systematic snapshots of the dataset at specific points in time. Weekly snapshots enable tracking changes over time by comparing consecutive snapshots, identifying modifications accurately. This method ensures a clear audit trail for regulatory compliance, leveraging Delta Lake's time travel feature designed for auditing. It provides a structured, reproducible approach to audit changes efficiently.
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How can you use Delta Lake's time travel feature to audit changes to a dataset over the last month for regulatory compliance?
A
Configure automated alerts in Azure Monitor to track and report changes in real-time, negating the need for time travel.
B
Write a Spark SQL query leveraging the DESCRIBE HISTORY Delta Lake command to list all changes, filtering by the timestamp for the last month.
C
Use the Databricks CLI to export data at the current version and manually compare it against a version from one month ago.
D
Implement a Databricks notebook that uses the VERSION AS OF syntax to snapshot the dataset at weekly intervals, comparing consecutive snapshots for changes.
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