The answer is data lakehouse. This architecture is the correct term because it uniquely merges the flexibility of a data lake—storing raw data in its original format in a scalable repository for schema-on-read transformations—with the reliability of a data warehouse, which enforces ACID transactions in a curated layer to ensure data integrity. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of modern analytics patterns, often appearing as a scenario where a solution needs both unstructured storage and transactional consistency. A common trap is confusing this with a traditional data warehouse or a simple data lake, but the key differentiator is the combination of raw storage and ACID compliance. Remember the memory tip: “Lake + House = Lakehouse,” where the lake holds the raw water (data) and the house provides the solid foundation (transactions).
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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 is designing an enterprise analytics solution. They store raw data in its original format in a scalable repository, apply schema and transformations at read time, and also maintain a curated layer that enforces ACID transactions for data reliability. This architecture combines the flexibility of a data lake with the reliability of a data warehouse. Which term best describes this modern data architecture?
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.
Correct. The data lakehouse architecture combines a data lake's flexibility (schema-on-read) with a data warehouse's ACID transactions and performance, often implemented with Delta Lake.
B
Data mart
Why wrong: Incorrect. A data mart is a focused subset of a data warehouse for a specific business function, not the entire architecture that blends lake and warehouse characteristics.
C
Operational database
Why wrong: Incorrect. An operational database is designed for transactional (OLTP) workloads, not for large-scale analytics with schema-on-read and ACID on curated data.
D
Data pipeline
Why wrong: Incorrect. A data pipeline is the set of processes that move and transform data, not the storage architecture itself.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data lakehouse
The data lakehouse architecture combines the flexibility of a data lake (storing raw data in its original format in a scalable repository) with the reliability of a data warehouse (enforcing ACID transactions in a curated layer). This allows schema-on-read transformations while maintaining data integrity, making it the correct term for the described design.
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 lakehouse
Why this is correct
Correct. The data lakehouse architecture combines a data lake's flexibility (schema-on-read) with a data warehouse's ACID transactions and performance, often implemented with Delta Lake.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data mart
Why it's wrong here
Incorrect. A data mart is a focused subset of a data warehouse for a specific business function, not the entire architecture that blends lake and warehouse characteristics.
✗
Operational database
Why it's wrong here
Incorrect. An operational database is designed for transactional (OLTP) workloads, not for large-scale analytics with schema-on-read and ACID on curated data.
✗
Data pipeline
Why it's wrong here
Incorrect. A data pipeline is the set of processes that move and transform data, not the storage architecture itself.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a data lakehouse with a data lake or data warehouse, missing the key combination of raw storage, schema-on-read, and ACID transactions that defines this modern architecture.
Detailed technical explanation
How to think about this question
Under the hood, a data lakehouse uses a metadata layer (e.g., Delta Lake, Apache Iceberg, or Apache Hudi) to enforce ACID transactions on top of object storage like Azure Data Lake Storage Gen2. This enables features like time travel, schema evolution, and concurrent read/write operations, which are critical for enterprise analytics where data reliability and consistency are required alongside the flexibility of storing raw data in formats like Parquet or JSON.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Data lakehouse — The data lakehouse architecture combines the flexibility of a data lake (storing raw data in its original format in a scalable repository) with the reliability of a data warehouse (enforcing ACID transactions in a curated layer). This allows schema-on-read transformations while maintaining data integrity, making it the correct term for the described design.
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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