LFC Delta Live Tables 1 — Questions and Answers
Question 1: What is Delta Live Tables (DLT) in the Databricks ecosystem?
- A declarative ETL framework for building reliable data pipelines (Correct answer)
- A visualization tool for Delta Lake tables
- A machine learning model deployment service
- A SQL query optimization engine
Correct answer: A declarative ETL framework for building reliable data pipelines
Delta Live Tables is a declarative ETL framework that automates pipeline infrastructure management and enforces data quality constraints.
Question 2: Which Python decorator is used to define a materialized Delta table in a DLT pipeline?
- @dlt.view
- @dlt.table (Correct answer)
- @dlt.stream
- @dlt.dataset
Correct answer: @dlt.table
The @dlt.table decorator defines a materialized table whose results are stored persistently as a Delta table.
Question 3: What is the primary purpose of DLT Expectations?
- To define schema for Delta tables
- To set pipeline scheduling intervals
- To enforce data quality constraints and handle invalid records (Correct answer)
- To configure cluster auto-scaling
Correct answer: To enforce data quality constraints and handle invalid records
DLT Expectations allow you to define data quality constraints with named rules and specify actions to take when records violate them.
Question 4: Which DLT pipeline execution mode processes all available data and then stops?
- Continuous mode
- Triggered mode (Correct answer)
- Streaming mode
- Incremental mode
Correct answer: Triggered mode
Triggered mode processes all available data at the time of the run and then terminates, making it suitable for scheduled batch workloads.
Question 5: What does the CONSTRAINT clause do in a DLT SQL pipeline?
- Defines table partitioning strategy
- Specifies data quality expectations with a name and condition (Correct answer)
- Sets access permissions on the table
- Configures pipeline storage location
Correct answer: Specifies data quality expectations with a name and condition
The CONSTRAINT clause in DLT SQL defines a named data quality expectation with a boolean condition that each record must satisfy.
Question 6: In DLT, what happens to records that violate an expectation configured with ON VIOLATION DROP ROW?
- The pipeline fails immediately
- The record is moved to a quarantine table
- The invalid record is excluded from the output dataset (Correct answer)
- A warning is logged but the record is kept
Correct answer: The invalid record is excluded from the output dataset
With ON VIOLATION DROP ROW, records failing the expectation are silently excluded from the output, and the metric is tracked in the event log.
Question 7: Which of the following is a key benefit of DLT over manually written Spark pipelines?
- DLT requires no compute resources to run
- DLT automatically manages dependencies between tables in the correct execution order (Correct answer)
- DLT does not support streaming data sources
- DLT only works with structured CSV files
Correct answer: DLT automatically manages dependencies between tables in the correct execution order
DLT infers the dependency graph between tables and automatically executes them in the correct order without manual orchestration.
LFC Delta Live Tables 1
What is Delta Live Tables (DLT) in the Databricks ecosystem?