DP-900 Describe an analytics workload on Azure Practice Question
Which TWO of the following are benefits of using a data lake architecture? (Choose two.)
⚠ Common exam trap
It's easy for candidates to confuse data lakes with data warehouses, assuming data lakes enforce ACID transactions and schema-on-write, or they overestimate built-in governance capabilities without realizing additional tools are required.
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
✓
Ability to store raw data in its native format
A data lake architecture is designed to store raw data in its native format without requiring schema-on-write transformations. This allows organizations to ingest data as-is from various sources, preserving the original structure and enabling schema-on-read flexibility for analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ACID transactions for all operations
Why it's wrong here
ACID transactions are not an inherent property of data lakes, which are designed for flexible, schema-on-read storage of massive volumes of diverse data. While some data lake platforms can be extended with technologies like Delta Lake or Apache Iceberg to provide ACID guarantees, this is not a built-in benefit of a data lake itself. The lack of native ACID support is a trade-off for the scalability and cost efficiency of raw file storage.
- ✗
Optimized for high-frequency OLTP workloads
Why it's wrong here
Data lakes are architected for analytical workloads, such as large-scale data exploration, machine learning, and reporting, not for high-frequency online transaction processing (OLTP). OLTP systems require low-latency, row-level updates and transactional integrity, which data lakes are not optimized to provide. Instead, data lakes typically handle append-heavy and read-heavy analytical queries over petabytes of data, making them unsuitable as a primary store for operational applications.
- ✓
Ability to store raw data in its native format
Why this is correct
A core benefit of a data lake is its ability to ingest data in its original, raw form without requiring pre-defined schemas or transformation. Whether the data is CSV, JSON, Parquet, Avro, images, or video, the data lake stores it exactly as it arrives, preserving granular detail for future analysis. This schema-on-read approach allows data engineers and scientists to define and apply structures when needed, enabling agile exploration and preventing the loss of potentially valuable raw information.
- ✗
Built-in data governance without additional tools
Why it's wrong here
Data lakes themselves do not come with built-in data governance capabilities such as data cataloging, lineage tracking, or fine-grained access policies; these require additional tools. For example, in Azure, implementing governance on top of a data lake typically involves services like Azure Purview or Microsoft Purview, which provide metadata management and policy enforcement. Without such tools, a data lake can become a disorganized swamp, but this responsibility is not automatically addressed by the data lake architecture.
- ✓
Support for structured, semi-structured, and unstructured data
Why this is correct
Data lakes uniquely support all data forms: structured data like relational tables, semi-structured data such as JSON or XML, and completely unstructured content like audio, video, and free-form text. This flexibility is a major advantage over traditional data warehouses, which are optimized for structured tabular data and often require pre-processing. By storing diverse data types in a single repository, a data lake enables organizations to perform analytics and machine learning across combined datasets that would otherwise be siloed.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
About these practice questions
Courseiva writes every DP-900 question from scratch — 820 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.