DA0-002 Data Concepts and Environments Practice Question
A data engineer is designing a data lake to store raw data from multiple sources, including JSON logs, CSV files, and Parquet files. The data will be used for both batch analytics and machine learning. The engineer must choose storage and processing strategies that align with the characteristics of a data lake. Which two of the following are core characteristics of a data lake? (Choose two.)
⚠ Common exam trap
The trap here is mixing data lake characteristics with those of a data warehouse, such as schema-on-write and pre-storage 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
✓
It stores data in its native format, including structured, semi-structured, and unstructured data.
A data lake stores data in its native format and applies schema-on-read, allowing raw data to be stored without upfront transformation. These two characteristics enable flexibility and support diverse data types. Enforcing schema-on-write, pre-storage transformation, and optimization for transactional processing are not core to data lakes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
It stores data in its native format, including structured, semi-structured, and unstructured data.
Why this is correct
A data lake is designed to ingest and store data in its original format, such as JSON, CSV, Parquet, or images, without requiring transformation. This flexibility supports diverse analytics and machine learning use cases. Storing native formats allows the organization to defer schema definition until read time, which is a defining characteristic of a data lake.
- ✗
It enforces schema-on-write, requiring data to be structured before storage.
Why it's wrong here
Schema-on-write is typical of data warehouses, where data is transformed and structured before loading. A data lake uses schema-on-read, allowing raw data to be stored without prior transformation. Enforcing schema-on-write would limit the flexibility to store diverse formats like JSON and Parquet and contradicts the core principle of a data lake.
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It is optimized for high-cost, low-volume transactional processing.
Why it's wrong here
Data lakes are typically built on cost-effective storage like object stores and are optimized for high-volume, low-cost storage and batch or analytical processing, not transactional processing. Transactional workloads are better suited to OLTP databases. This option describes a characteristic that is opposite to the design goals of a data lake.
- ✓
It uses schema-on-read, applying structure only when the data is queried.
Why this is correct
Schema-on-read means the data lake does not enforce a schema when writing data; instead, the schema is applied when the data is read or queried. This approach supports flexibility and allows different consumers to interpret the same data in different ways. It is a core characteristic that distinguishes data lakes from data warehouses.
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It requires all data to be transformed and cleaned before it can be stored.
Why it's wrong here
Transforming and cleaning data before storage is a characteristic of ETL pipelines feeding a data warehouse. A data lake allows raw, unprocessed data to be stored first, with transformations applied later when the data is read. Requiring pre-storage transformation would defeat the purpose of a data lake and reduce its agility.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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