DA0-002 Data Concepts and Environments Practice Question
An organization is implementing a data lake to store raw data from various sources. Which THREE characteristics are typically associated with a data lake compared to a data warehouse?
⚠ Common exam trap
CompTIA often tests the misconception that data lakes require data transformation before loading (schema-on-write), when in fact they use schema-on-read, allowing raw data storage without upfront transformation.
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
✓
Supports batch and real-time processing
Data lakes are designed to ingest and process data in both batch and real-time streaming modes, unlike data warehouses which primarily handle batch ETL workloads. This flexibility is enabled by technologies like Apache Kafka for streaming and Apache Spark for batch processing, allowing the data lake to support diverse ingestion patterns without pre-schematization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Supports batch and real-time processing
Why this is correct
Correct: Data lakes handle both processing modes.
- ✓
Stores data in its native format
Why this is correct
Correct: Raw data is stored as-is.
- ✓
Schema-on-read approach
Why this is correct
Correct: Data lakes apply schema when reading, not when writing.
- ✗
Supports only structured data
Why it's wrong here
Incorrect: Data lakes handle all data types.
- ✗
Requires data transformation before loading
Why it's wrong here
Incorrect: Data warehouses require ETL; data lakes use ELT.
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