A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to…

The correct answer is D. Delta Live Tables. Delta Live Tables (DLT) is Databricks' declarative ETL framework that includes built-in data quality monitoring via 'expectations' - constraints you define on your datasets. DLT automatically tracks constraint violations, can quarantine or drop bad records, and provides metrics…

Submitted by olafpl· Apr 18, 2026Monitoring and Logging

Question

A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level. Which of the following tools can the data engineer use to solve this problem?

Options

AUnity Catalog
BData Explorer
CDelta Lake
DDelta Live Tables
EAuto Loader

How the community answered

(38 responses)

A
8% (3)
B
3% (1)
C
3% (1)
D
84% (32)
E
3% (1)

Explanation

Delta Live Tables (DLT) is Databricks' declarative ETL framework that includes built-in data quality monitoring via 'expectations' - constraints you define on your datasets. DLT automatically tracks constraint violations, can quarantine or drop bad records, and provides metrics dashboards for quality monitoring over time. This makes it ideal for automating data quality monitoring pipelines. Unity Catalog handles governance/access control, Data Explorer is a UI tool, Delta Lake provides storage format features, and Auto Loader handles incremental file ingestion - none of these provide automated quality constraint enforcement like DLT.

Topics

#Data quality monitoring#Automated data pipelines#Delta Live Tables#Data ingestion

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