DA0-002 Schema-on-write Practice Question
An organization uses a data warehouse for analytics. Which two characteristics are typical of a data warehouse compared to a data lake? (Select TWO.)
⚠ Common exam trap
Watch out — candidates often confuse the ETL/ELT processes, incorrectly associating ELT with data warehouses, or assume data warehouses can handle all data types like unstructured data, which is a key differentiator of data lakes.
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
✓
Optimized for complex queries and reporting
A data warehouse is optimized for complex queries and reporting (A) because it uses schema-on-write (D), meaning data is transformed and structured before loading to support fast analytical queries. In contrast, a data lake uses schema-on-read and stores raw data. ELT is typical of data lakes, not data warehouses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Optimized for complex queries and reporting
Why this is correct
Data warehouses are designed for analytics.
- ✗
Typically uses ELT process
Why it's wrong here
ELT is more common with data lakes; warehouses often use ETL.
- ✗
Stores raw data in native format
Why it's wrong here
That is a characteristic of data lakes.
- ✓
Uses schema-on-write
Why this is correct
Data warehouses apply schema when loading data.
- ✗
Supports all data types including unstructured
Why it's wrong here
Data warehouses primarily handle structured data.
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