Data Engineering ETL and ELT Pipelines 2

Question 1

In an ELT pipeline, where does the bulk of data transformation logic execute?

Accepted Answer

In the target data warehouse after loading

Answer

ELT loads raw data into the warehouse first and uses the warehouse's compute to transform it.

Question 2

A pipeline reprocesses the entire source table every night instead of only changed rows. Which technique would reduce this load?

Accepted Answer

Incremental loading with change data capture

Answer

Incremental loading via CDC processes only inserted, updated, or deleted records since the last run.

Question 3

Which scenario most favors ELT over traditional ETL?

Accepted Answer

A cloud warehouse with elastic, scalable compute like Snowflake or BigQuery

Answer

ELT thrives when the destination warehouse provides cheap, scalable compute to transform raw loaded data.

Question 4

What is the primary purpose of a staging area in an ETL pipeline?

Accepted Answer

A temporary landing zone for raw data before transformation

Answer

Staging holds extracted raw data temporarily so transformations can run without touching source systems.

Question 5

An idempotent pipeline run guarantees what behavior?

Accepted Answer

Running it multiple times produces the same final result

Answer

Idempotency means repeated executions yield identical state, preventing duplicates on retry.

Data Engineering Practice Test