DA0-002 Data Concepts and Environments Practice Question
A company is migrating its data pipeline from on-premises to the cloud. The current ETL process transforms data before loading into a data warehouse. The new architecture will use ELT instead. Which THREE of the following are advantages of ELT over traditional ETL? (Select 3)
⚠ Common exam trap
DA0-002 often tests whether candidates confuse ELT's benefits (raw data retention, cloud compute leverage, schema-on-read) with ETL's strengths (pre-load data quality, reduced storage), leading them to pick options that actually describe ETL.
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
✓
Provides ability to reprocess raw data if transformation logic changes
Option B is correct because ELT loads raw data into the target system first, preserving the original data so transformations can be re-run whenever business logic or requirements change, without needing to re-extract from source systems. Option C is correct because ELT pushes transformation work down to the cloud data warehouse (e.g., Snowflake, BigQuery, Redshift), leveraging its massively parallel processing and elastic compute rather than relying on a separate ETL server. Option E is correct because ELT commonly pairs with schema-on-read, where the schema is applied at query time, allowing flexible analysis of raw, semi-structured, or evolving data. Option A is not correct because ELT typically defers quality checks and transformations until after loading, whereas ETL enforces data quality before loading. Option D is not correct because ELT generally stores raw data in addition to transformed data, which tends to increase rather than reduce storage requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ensures data quality before loading
Why it's wrong here
ELT loads raw data before any transformation, so quality issues surface after loading rather than being prevented beforehand. Validating and cleansing data prior to load is what ETL provides, making it the right choice when downstream consumers cannot tolerate raw or malformed records.
- ✓
Provides ability to reprocess raw data if transformation logic changes
Why this is correct
ELT loads raw data first, so transformations run inside the warehouse against persisted source data. If transformation logic changes, the raw layer is reprocessed without re-extracting from source systems, satisfying the stem's requirement for an advantage unavailable in transform-before-load ETL.
- ✓
Leverages the processing power of the cloud data warehouse
Why this is correct
ELT pushes transformation work into the cloud data warehouse, using its elastic compute and columnar engines rather than a separate on-premises ETL server. This satisfies the migration scenario's aim of exploiting cloud scalability, an advantage ETL's external transformation tier cannot match.
- ✗
Reduces storage costs by storing only transformed data
Why it's wrong here
ELT loads raw data into the warehouse first, so it stores untransformed data and typically increases storage consumption rather than reducing it. Storing only transformed data describes traditional ETL, where transformation occurs before loading, which would be the right approach when warehouse storage is constrained.
- ✓
Allows for schema-on-read, enabling flexible analysis
Why this is correct
ELT retains raw data and applies schema at query time, so analysts can interpret the same dataset differently without reloading. This schema-on-read flexibility satisfies the stem's advantage criterion, unlike ETL's schema-on-write, which fixes structure before loading.
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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
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