Databricks Certified Data Engineer - Associate

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:

@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'")

@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.