The answer is data lake. A data lake is the correct choice because it is specifically architected to ingest and store massive volumes of raw data in its native format—whether structured, semi-structured, or unstructured—without requiring any upfront schema definition or transformation. This directly matches the scenario of collecting IoT sensor streams, social media feeds, and legacy CSV files as-is, preserving the data for data scientists to later apply machine learning models or run ad-hoc queries. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your ability to distinguish between a data lake and a data warehouse, with a common trap being to choose a data warehouse because it also stores data, but a data warehouse requires schema-on-write and transformation before loading. Remember the key distinction: a data lake stores raw data for flexible analysis later, while a data warehouse stores processed data for structured reporting. A helpful memory tip is to think of a data lake as a “store now, think later” approach, whereas a data warehouse is “think first, store later.”
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company collects data from multiple sources: IoT sensor streams, social media feeds, and CSV files from legacy systems. They want to store all this data in its original format without any transformation, so that data scientists can later apply machine learning models or run ad-hoc queries. Which data storage pattern best describes this approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: A data warehouse stores structured, transformed data optimized for reporting and analysis, not raw data in its original format.
B
Data lake
A data lake stores data in its native format without transformation, supporting diverse data types and ad-hoc exploration by data scientists.
C
Relational database
Why wrong: A relational database requires a fixed schema and is designed for transactional processing, not for storing raw, unstructured data at scale.
D
Data mart
Why wrong: A data mart is a subset of a data warehouse focused on a specific business area and still requires pre-transformed, structured data.
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 vast amounts of raw data in its native format (structured, semi-structured, or unstructured) without requiring upfront schema or transformation. This aligns perfectly with the scenario of ingesting IoT streams, social media feeds, and CSV files as-is, enabling data scientists to later apply machine learning or run ad-hoc queries directly against the raw data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 warehouse
Why it's wrong here
A data warehouse stores structured, transformed data optimized for reporting and analysis, not raw data in its original format.
✓
Data lake
Why this is correct
A data lake stores data in its native format without transformation, supporting diverse data types and ad-hoc exploration by data scientists.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Relational database
Why it's wrong here
A relational database requires a fixed schema and is designed for transactional processing, not for storing raw, unstructured data at scale.
✗
Data mart
Why it's wrong here
A data mart is a subset of a data warehouse focused on a specific business area and still requires pre-transformed, structured data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse a data lake with a data warehouse, assuming both are for analytics, but the key differentiator is that a data lake stores raw, unprocessed data while a data warehouse requires transformation and schema-on-write.
Detailed technical explanation
How to think about this question
Under the hood, a data lake typically uses a distributed file system (e.g., HDFS) or cloud object storage (e.g., Azure Data Lake Storage Gen2) with a flat architecture, allowing data to be stored in formats like Parquet, Avro, or JSON without schema enforcement. A key subtlety is that while data lakes support schema-on-read, they require proper metadata management (e.g., using a catalog like Apache Hive) to avoid becoming a 'data swamp' where data is unmanageable. In a real-world scenario, a company might ingest IoT sensor data as raw JSON blobs, social media feeds as CSV exports, and legacy CSV files into the same lake, then use Azure Synapse or Databricks to apply ML models without ever altering the original files.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Data lake — A data lake is designed to store vast amounts of raw data in its native format (structured, semi-structured, or unstructured) without requiring upfront schema or transformation. This aligns perfectly with the scenario of ingesting IoT streams, social media feeds, and CSV files as-is, enabling data scientists to later apply machine learning or run ad-hoc queries directly against the raw data.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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