Databricks Certified Data Engineer - Associate

Delta Live Tables (DLT) is the correct tool for automating data quality monitoring. DLT provides built-in data quality features that allow data engineers to define expectations and constraints on their data pipelines. When data quality issues are detected, DLT can automatically handle them through various actions like dropping records, quarantining data, or halting the pipeline.

Key features of Delta Live Tables for data quality:

1. Expectations: Define constraints and expectations on data (e.g., @expect("valid_id", "id IS NOT NULL"))
2. Automated monitoring: Continuously monitors data quality during pipeline execution
3. Quality metrics: Tracks and reports on data quality metrics
4. Automatic handling: Can be configured to take specific actions when expectations are violated

Why other options are incorrect:

• Unity Catalog: Primarily for data governance, cataloging, and access control, not specifically for automated quality monitoring
• Data Explorer: Not a standard Databricks tool for data quality monitoring
• Delta Lake: Provides ACID transactions and schema enforcement but doesn't have built-in automated quality monitoring features
• Auto Loader: Used for incremental data ingestion, not specifically for data quality monitoring