Databricks Certified Data Engineer - Associate

Delta Live Tables (DLT) provides several key benefits over standard Spark and Delta Lake pipelines on Databricks:

Correct Answer: A - The ability to declare and maintain data table dependencies

Explanation: Delta Live Tables provides a declarative framework where you can explicitly define dependencies between tables using SQL or Python. This is a significant advantage over standard pipelines where you need to manually manage execution order and dependencies. DLT automatically handles:

• Dependency resolution and execution ordering
• Lineage tracking between tables
• Automatic retry of failed dependencies
• Incremental processing based on dependency changes

Why other options are not correct:

B - The ability to write pipelines in Python and/or SQL: This is also possible with standard Spark and Delta Lake pipelines on Databricks, so it's not a unique benefit of DLT.

C - The ability to access previous versions of data tables: This is a feature of Delta Lake itself (time travel), not specific to DLT.

D - The ability to automatically scale compute resources: This is a feature of Databricks clusters and autoscaling, not unique to DLT.

E - The ability to perform batch and streaming queries: Standard Spark pipelines on Databricks can also handle both batch and streaming workloads.

DLT's primary value proposition is its declarative approach to pipeline development, automatic dependency management, data quality enforcement, and simplified operationalization of data pipelines.