DP-900 Describe core data concepts Practice Question
Which THREE of the following are characteristics of a data lake compared to a data warehouse?
⚠ Common exam trap
Microsoft often tests the misconception that data lakes are just 'dumping grounds' without any structure, but the trap here is confusing ACID guarantees (which are optional and engine-specific) as a universal characteristic of data lakes, or assuming data lakes only handle unstructured data when they actually support all data types.
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 lakes store data in its native or raw format.
A data lake stores data in its native or raw format, meaning it does not require transformation or schema definition at the time of ingestion. This allows organizations to retain the original fidelity of the data, which is a fundamental distinction from a data warehouse that typically transforms and structures data before loading (ETL). In Azure, Azure Data Lake Storage (ADLS) Gen2 supports storing any file format (e.g., Parquet, CSV, JSON, binary) without preprocessing.
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 lakes store data in its native or raw format.
Why this is correct
Data lakes preserve ingested data exactly as it arrives, without pre-processing, transformation, or format conversion before landing. This native/raw retention lets organizations re-purpose the same underlying file later for diverse workloads, since no information has been discarded for a particular analysis. It is a key difference from data warehouses, which clean and transform data during the load process.
- ✓
Data lakes store structured, semi-structured, and unstructured data.
Why this is correct
A data lake can simultaneously hold structured tables (like CSV or Parquet with defined columns), semi-structured data (JSON, XML, logs), and unstructured files (PDFs, images, audio, video). Because storage does not impose a fixed relational schema, these varied types can coexist in one repository for centralized analytics and machine learning. This breadth of formats makes the lake a universal landing zone for enterprise data.
- ✓
Data lakes use schema-on-read rather than schema-on-write.
Why this is correct
With schema-on-read, the schema is projected by the query engine at the time data is accessed, not fixed when data is written. This allows analysts to define or change structure per workload without moving or reformatting the underlying files, but it also pushes data quality and compatibility issues to the reader. In contrast, schema-on-write systems like conventional warehouses validate and enforce structure prior to loading.
- ✗
Data lakes guarantee ACID transactions across all data.
Why it's wrong here
ACID transactions require coordinated atomicity, consistency, isolation, and durability, which a generic data lake does not natively provide across all stored objects. Standard object-store commits are not transactional, so concurrent reads/writes can yield partial or inconsistent results unless a transactional layer such as Delta Lake, Apache Iceberg, or Apache Hudi is added. Even then, ACID guarantees apply only to tables that opt into those formats, not to every file in the lake.
- ✗
Data lakes store only structured data.
Why it's wrong here
This claim contradicts the core design of data lakes, which intentionally ingest structured, semi-structured, and unstructured data without discrimination. A system that stores only structured data is a relational database or classic data warehouse, not a data lake. Unstructured and semi-structured inputs (documents, sensor streams, clickstream logs, multimedia) are common lake payloads.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
ETL
ETL stands for Extract, Transform, Load, a process that moves data from multiple source systems into a single database, data warehouse, or data lake for analysis and reporting.
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
About these practice questions
This DP-900 question is part of Courseiva's 820-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.