DA0-002 Data Concepts and Environments Practice Question
A data engineer is designing a data pipeline where raw data is loaded into a cloud data warehouse (Snowflake) and then transformed using SQL. This approach is called:
⚠ Common exam trap
DA0-002 often tests the ETL vs. ELT distinction — candidates pick ETL by default because it is the older, more familiar term, ignoring the clue that transformation happens after loading into the warehouse.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ELT
ELT (Extract, Load, Transform) loads raw data into the target warehouse first and then performs transformations using the warehouse's compute engine — exactly the pattern described with Snowflake. This leverages Snowflake's elastic compute and SQL capabilities to transform data in place, avoiding a separate transformation tier. ETL, by contrast, transforms data before loading it into the warehouse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
ELT
Why this is correct
ELT loads raw data into Snowflake before any transformation, satisfying the stem's requirement that data lands in the warehouse first and SQL runs afterwards. Unlike ETL, where transformation precedes loading, here Snowflake's compute performs the SQL transformations, matching the described pipeline exactly.
- ✗
ETL
Why it's wrong here
ETL extracts, transforms then loads, so transformation precedes loading. The stem loads raw data first and transforms afterwards inside Snowflake, which is ELT; ETL would be correct where a staging server transforms records before they reach the warehouse.
- ✗
Data migration
Why it's wrong here
Data migration moves data between systems, platforms or schemas during a one-off transition, such as retiring an on-premises warehouse for Snowflake. The stem describes a recurring pipeline that loads raw data and transforms it with SQL, which migration does not denote.
- ✗
Data wrangling
Why it's wrong here
Data wrangling covers cleaning and reshaping messy source data before or during loading, not the load-then-transform-in-warehouse pattern described. It would be the right term when analysts manually reconcile inconsistent formats, duplicates or missing values in raw extracts.
Go deeper
Related to this question
About these practice questions
One of 1,004 original DA0-002 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.