Answer: Delta Live Tables

## Explanation Delta Live Tables (DLT) is the correct answer because it provides built-in data quality monitoring capabilities. Here's why: **Why Delta Live Tables is correct:** 1. **Built-in Data Quality Features**: DLT includes expectations and constraints that allow you to define data quality rules directly in your pipeline code. 2. **Automated Monitoring**: You can set up expectations that automatically monitor data quality during pipeline execution. 3. **Quality Enforcement**: DLT can be configured to either: - **Fail** the pipeline when data quality expectations are not met - **Drop** records that don't meet expectations - **Quarantine** problematic records for further investigation 4. **Real-time Quality Checks**: Quality checks happen as part of the pipeline execution, providing immediate feedback on data quality issues. **Why other options are incorrect:** - **A. Auto Loader**: This is for incremental data ingestion from cloud storage, not specifically for data quality monitoring. - **B. Unity Catalog**: This is for data governance, security, and metadata management, not for automated data quality monitoring in pipelines. - **C. Delta Lake**: This provides ACID transactions, time travel, and schema enforcement, but doesn't have built-in automated quality monitoring features like DLT does. **Example DLT quality expectation syntax:** ```python @dlt.expect("valid_email", "email IS NOT NULL AND email LIKE '%@%'") @dlt.expect_or_drop("positive_salary", "salary > 0") @dlt.expect_or_fail("valid_date", "purchase_date > '2020-01-01'") ``` Delta Live Tables is specifically designed to help data engineers build reliable, production-quality data pipelines with built-in data quality controls.

Author: Keng Suppaseth