Data Engineering Associate with Databricks Practice Exam

What happens to records that violate data processing constraints?

1. They are deleted from the target dataset.

2. They are added to the target dataset and logged as invalid.

3. They are ignored and not processed.

4. They are sent to a separate error log for review.

The correct answer is: They are added to the target dataset and logged as invalid.

When dealing with records that violate data processing constraints, it is critical to maintain data integrity while ensuring that issues can be tracked and analyzed. Adding those records to the target dataset and logging them as invalid provides a comprehensive way to handle discrepancies. This means that downstream processes can still operate using the valid data while having a clear record of what was problematic. This approach not only preserves the integrity of the processing workflow by not excluding potentially important data but also enables data engineers and analysts to review and understand the reasons behind the invalid entries later. By logging as invalid, it highlights areas that may require correction or additional validation, facilitating improved data quality over time. The alternative approaches, like sending records to an error log for review or ignoring them, would either obscure potential issues or lead to loss of data transparency, hindering future data quality assessments and corrections. Deleting these records outright also creates potential gaps in datasets that might need to be investigated. Thus, the method of adding invalid data while logging it ensures a balance between operational efficiency and data accountability.