DP-750: Implementing Data Engineering Solutions Using Azure DatabricksDemo

Question

You have an Azure Databricks workspace enabled for Unity Catalog that contains a managed Delta table named Sales.

Sales stores transaction data and has the following columns:

transaction_id (string)
transaction_date (date)
amount (decimal)

You need to implement these data-quality requirements by using table-level data-quality enforcement:

amount must be greater than 0.
transaction_id must never be null.

Invalid records must be rejected when they are written to the Sales table.

What should you do?

A Use a SELECT statement with WHERE conditions to validate the data before querying.
B Create a view that filters out rows where transaction_id is null or amount is less than or equal to 0.
C Add a NOT NULL constraint to transaction_id and a CHECK constraint to amount.
D Configure row-level security (RLS) where transaction_id is null or amount is less than or equal to 0.

Explanation

A NOT NULL constraint prevents writes with a null transaction_id, and a CHECK constraint requiring amount > 0 rejects rows with zero or negative amounts. These are enforced Delta table constraints; violating either causes the write transaction to fail.

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DP-750: Implementing Data Engineering Solutions Using Azure DatabricksDemo

AstroTutor

Notes

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