ETL Testing ETL Data Extraction Techniques 2

Question 1

In ETL testing, what is the purpose of a 'source-to-staging' validation?

Accepted Answer

Confirm that raw extracted data landed intact in the staging area

Answer

Verify transformations were applied correctly

Answer

Check target table indexes

Answer

Validate final report output

Question 2

A trigger-based extraction fires a database trigger on DML events. What is a key risk of this approach?

Accepted Answer

It adds performance overhead to the source system

Answer

It cannot capture DELETE operations

Answer

It requires a data warehouse license

Answer

It only works on NoSQL databases

Question 3

What is 'push extraction' in ETL architecture?

Accepted Answer

The source system sends data to the ETL layer when changes occur

Answer

The ETL engine queries the source on a schedule

Answer

Data is manually uploaded by end users

Answer

The target database requests data from staging

Question 4

During ETL extraction testing, you notice the extracted file has fewer rows than the source query returns. What is the FIRST diagnostic step?

Accepted Answer

Check for extraction filters or WHERE clause conditions applied during pull

Answer

Rebuild the target warehouse schema

Answer

Delete and re-run the full load

Answer

Increase database connection pool size

Question 5

Which data extraction method is BEST for near-real-time ETL pipelines that need sub-minute latency?

Accepted Answer

Streaming CDC via Kafka or similar messaging

Answer

Nightly full table scan

Answer

Weekly incremental batch

Answer

Manual file drop

Question 6

What is a 'watermark' in incremental ETL extraction?

Accepted Answer

A stored timestamp or ID used to identify where the last extraction ended

Answer

A data encryption marker

Answer

A schema version number

Answer

A checksum of extracted records