DA0-002 Data Concepts and Environments Practice Question
A data architect needs to store raw data from various sources, including social media feeds and log files, for future analysis. The data may be used for machine learning and ad-hoc queries. Which storage solution is most appropriate for storing raw data in its native format?
⚠ Common exam trap
Many exam-takers confuse a data lake with a data warehouse, assuming both are for analytics, but the key distinction is that a data warehouse requires structured, transformed data while a data lake preserves raw, native-format data.
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 raw data in its native format, including unstructured and semi-structured data from sources like social media feeds and log files. It supports schema-on-read, making it ideal for future machine learning and ad-hoc queries without requiring upfront transformation. This aligns directly with the requirement to preserve raw data for flexible analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data lake
Why this is correct
A data lake stores raw data in its native format without schema enforcement, accommodating social media feeds and log files. This satisfies the requirement for future machine learning and ad-hoc queries, unlike warehouses that demand predefined structure.
- ✗
Data mart
Why it's wrong here
A data mart is designed for structured, aggregated data optimised for specific departmental reporting, not for storing raw, unprocessed data from social media feeds and log files. It fails because it requires schema-on-write transformation, which would alter the native format and prevent the flexible, ad-hoc queries and machine learning workloads described in the stem. It is tempting because data marts provide fast, pre-aggregated query performance for business intelligence; they would be correct if the requirement were to serve curated, subject-specific reports to a single department.
- ✗
Relational database
Why it's wrong here
A relational database enforces a fixed schema, so it cannot store raw social media feeds and log files in their native formats without transformation. It is tempting because it supports ad-hoc SQL queries, and it would be correct when data is structured, with defined columns and relationships, rather than schema-less raw input.
- ✗
Data warehouse
Why it's wrong here
A data warehouse stores transformed, modelled data optimised for structured analytical queries, not raw native-format files. It is tempting because it supports analytics and machine learning on curated data, and it would be correct when the requirement is querying cleansed, integrated data rather than retaining original source formats.
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