DA0-002 Data Concepts and Environments Practice Question
A business needs to store large volumes of raw data in its native format for future analytics. Which storage architecture is most appropriate?
⚠ Common exam trap
Many candidates confuse a data warehouse with a data lake, assuming both are for analytics, but the key differentiator is that a data warehouse requires schema-on-write and processed data, while a data lake stores raw data in native format.
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
✓
Data lake
A data lake is designed to store large volumes of raw data in its native format (structured, semi-structured, or unstructured) without requiring a predefined schema. This makes it ideal for future analytics where the data schema may not yet be known, as it supports schema-on-read rather than schema-on-write.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Relational database
Why it's wrong here
A relational database enforces a fixed schema and stores structured rows, requiring transformation before loading; it cannot hold large volumes of raw, native-format data for future analytics. It is tempting because it is a familiar store, but that suits transactional workloads. A data lake preserves raw data in native format with schema applied at read time.
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Data lake
Why this is correct
A data lake stores raw data in its native format, such as JSON, Parquet or CSV, without requiring a predefined schema. This satisfies the requirement to retain large volumes of unprocessed data for future, undetermined analytics workloads.
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Operational data store
Why it's wrong here
An operational data store holds integrated, current data for near-real-time operational reporting, with schema applied on write; it does not retain raw, native-format files for later analytics. It is tempting because it stores data centrally, but that is for transactional reporting. A data lake stores raw data in native format until schema-on-read analysis.
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Data warehouse
Why it's wrong here
A data warehouse stores data that has already been modelled, transformed and conformed to a schema, so raw native-format files must be processed before loading. It is tempting because it serves analytics, but it requires schema-on-write. A data lake accepts raw data in native format and defers schema until query.
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