LFC ETL & Data Integration 2

Question 1

In a Delta Lake ETL pipeline, what is the primary purpose of the MERGE INTO statement?

Accepted Answer

To perform upserts by matching source and target rows

Answer

MERGE INTO enables upsert operations by matching source and target rows on a key and then inserting, updating, or deleting based on match conditions.

Question 2

Which Change Data Capture (CDC) mode in Delta Lake reads only the changes since the last processed version?

Accepted Answer

Incremental CDC using table history

Answer

Incremental CDC leverages Delta Lake's transaction log to read only the rows that changed since a specified version or timestamp.

Question 3

When ingesting JSON data with a schema that may evolve, which Spark option should you enable to handle new fields without failing?

Accepted Answer

mergeSchema

Answer

Setting mergeSchema to true allows Spark to union the new JSON schema with the existing Delta table schema when new columns appear.

Question 4

A pipeline reads from Kafka and writes to a Delta Lake table every 5 minutes. Which Structured Streaming trigger is most appropriate?

Accepted Answer

Trigger.ProcessingTime('5 minutes')

Answer

Trigger.ProcessingTime('5 minutes') causes the streaming query to execute micro-batches on a fixed 5-minute interval.

Question 5

What Databricks feature allows you to declaratively define ETL pipelines with automatic dependency resolution and retries?

Accepted Answer

Delta Live Tables (DLT)

Answer

Delta Live Tables lets you define pipeline steps as live tables or views, and Databricks automatically manages execution order, retries, and data quality.

(LFC) Lakehouse Fundamentals Certification Practice Test

(LFC) Lakehouse Fundamentals Certification Practice Test

LFC ETL & Data Integration 2