LFC Delta Live Tables

Question 1

What is Delta Live Tables (DLT) in the Databricks ecosystem?

Accepted Answer

A declarative ETL framework for building reliable data pipelines

Answer

A visualization tool for Delta Lake tables

Answer

A machine learning model deployment service

Answer

A SQL query optimization engine

Question 2

Which Python decorator is used to define a materialized Delta table in a DLT pipeline?

Accepted Answer

@dlt.table

Answer

@dlt.view

Answer

@dlt.stream

Answer

@dlt.dataset

Question 3

What is the primary purpose of DLT Expectations?

Accepted Answer

To enforce data quality constraints and handle invalid records

Answer

To define schema for Delta tables

Answer

To set pipeline scheduling intervals

Answer

To configure cluster auto-scaling

Question 4

Which DLT pipeline execution mode processes all available data and then stops?

Accepted Answer

Triggered mode

Answer

Continuous mode

Answer

Streaming mode

Answer

Incremental mode

Question 5

What does the CONSTRAINT clause do in a DLT SQL pipeline?

Accepted Answer

Specifies data quality expectations with a name and condition

Answer

Defines table partitioning strategy

Answer

Sets access permissions on the table

Answer

Configures pipeline storage location

Question 6

In DLT, what happens to records that violate an expectation configured with ON VIOLATION DROP ROW?

Accepted Answer

The invalid record is excluded from the output dataset

Answer

The pipeline fails immediately

Answer

The record is moved to a quarantine table

Answer

A warning is logged but the record is kept

Question 7

Which of the following is a key benefit of DLT over manually written Spark pipelines?

Accepted Answer

DLT automatically manages dependencies between tables in the correct execution order

Answer

DLT requires no compute resources to run

Answer

DLT does not support streaming data sources

Answer

DLT only works with structured CSV files