Exam DP-750 Topic 1 Question 70 Discussion
Actual exam question for Microsoft's DP-750 exam
Question #: 70
Topic #: 1
Question #: 70
Topic #: 1
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following 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 data is written to the Sales table.
What should you do?
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following 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 data is written to the Sales table.
What should you do?
Suggested Answer: D Vote an answer
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don ' t prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely. Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don ' t prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely. Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints
by Eleanore at Aug 08, 2026, 06:23 AM
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