CompTIA Data+ DA0-002 (V2) Practice Question

A data lake is the correct choice because it is a centralized repository designed to store vast quantities of data in its native, raw format. This approach supports structured, semi-structured, and unstructured data types. Data lakes use a 'schema-on-read' strategy, meaning the structure is applied when the data is read for analysis, not when it is stored. This provides the flexibility required by the data science team to explore raw data and experiment with different analytical models.

A relational database is incorrect because it requires a predefined schema (schema-on-write) and is best suited for structured data, not the mix of structured, semi-structured, and unstructured data described in the scenario.

A data warehouse is also incorrect. While it can store large amounts of data for analysis, it requires data to be cleaned, transformed, and structured before being loaded (schema-on-write). This would not allow the data science team to work with the raw, unaltered data.

A data mart is a subset of a data warehouse focused on a specific business line or department. It is also based on structured, pre-processed data and lacks the scale and flexibility needed to store raw data from so many different sources for undefined future analysis.