Databricks Certified Data Engineer - Associate
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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?
Real Exam
Community
KKeng
A
Unity Catalog
B
Data Explorer
C
Delta Lake
D
Delta Live Tables
E
Auto Loader
Explanation:
Delta Live Tables (DLT) is the correct tool for automating data quality monitoring in Databricks pipelines. DLT provides built-in data quality features including:
- Expectations framework: Allows you to define data quality constraints
- Automated monitoring: Continuously monitors data quality during pipeline execution
- Quality metrics tracking: Records and tracks data quality metrics over time
- Pipeline health monitoring: Provides visibility into data quality issues
- Automated remediation: Can be configured to handle quality violations (e.g., quarantine bad data, stop pipeline)
While other options have some related functionality:
- Unity Catalog (A): Primarily for data governance, cataloging, and access control
- Data Explorer (B): For exploring and visualizing data, not automated quality monitoring
- Delta Lake (C): Provides ACID transactions and schema enforcement, but not automated quality monitoring
- Auto Loader (E): For incremental data ingestion, not quality monitoring
DLT's data quality features are specifically designed to automate the monitoring and management of data quality in production pipelines.
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