DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Which THREE of the following are characteristics of a data lake compared to a data warehouse?
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.
Option A is correct because 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.
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 lakes store data in its native or raw format.
Why this is correct
Data lakes store raw data in its original format.
Related concept
Read the scenario before looking for a memorised answer.
✓
Data lakes store structured, semi-structured, and unstructured data.
Why this is correct
Data lakes handle all data formats.
Related concept
Read the scenario before looking for a memorised answer.
✓
Data lakes use schema-on-read rather than schema-on-write.
Why this is correct
Schema-on-read means the schema is applied when reading the data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data lakes guarantee ACID transactions across all data.
Why it's wrong here
Data lakes do not inherently guarantee ACID transactions.
✗
Data lakes store only structured data.
Why it's wrong here
Data lakes store all data types, including unstructured.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Schema-on-read means the data structure is interpreted at query time (e.g., using Spark or Hive), allowing flexibility for ad-hoc analytics, while schema-on-write enforces structure at ingestion, typical of data warehouses. In practice, a data lake can store petabytes of raw IoT sensor data as JSON blobs, and a schema is applied only when a user runs a SELECT statement via PolyBase or Azure Synapse Serverless SQL. This decoupling of storage and compute is a key architectural pattern in modern data platforms.
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 lakes store data in its native or raw format. — Option A is correct because 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.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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