Question 618 of 820
DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Candidates often 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.
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 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.
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
A data lakehouse is the correct choice because it combines the cost-effective, schema-on-read flexibility of a data lake with the ACID transactions, indexing, and SQL analytics of a data warehouse. This unified architecture lets an enterprise store raw data in open formats (e.g., Parquet) while providing data reliability, time travel, and concurrency control often via Delta Lake, Apache Iceberg, or Hudi. It directly matches the requirement for raw storage plus analytical curation.
- ✗
Data mart
Why it's wrong here
A data mart is a focused, department-specific subset of a data warehouse, typically containing aggregated and curated data for a single business function such as sales or finance. It is a downstream presentation layer, not the overarching storage architecture that merges raw lake flexibility with warehouse reliability. In an enterprise design, data marts are built on top of a warehouse or lakehouse, so they cannot serve as the primary raw-data storage architecture.
When this WOULD be correct
A question that asks: 'A sales department needs a dedicated, read-optimized dataset for reporting on regional sales, sourced from the enterprise data warehouse. Which component should they use?' The correct answer would be data mart.
- ✗
Operational database
Why it's wrong here
An operational database is optimized for OLTP workloads, handling frequent, short, row-level transactions with low latency and high concurrency. It is not architecturally suited for large-scale analytical scans, schema-on-read processing, or ACID over massive raw datasets. While it may source data for analytics, its storage and indexing model is fundamentally different from the lakehouse pattern needed for enterprise-scale analytics.
When this WOULD be correct
A question that asks: 'Which type of database is optimized for high-volume, low-latency transaction processing, such as order entry or banking transactions?' would have operational database as the correct answer.
- ✗
Data pipeline
Why it's wrong here
A data pipeline is a series of processes that ingest, transform, validate, and move data from sources to destinations, often toward a storage platform like a lake or warehouse. It is an orchestration mechanism, not a storage architecture itself, so it does not describe how raw data is stored or queried. While pipelines are essential to feed a lakehouse, they are the means of data flow, not the end state.
When this WOULD be correct
A data pipeline would be the correct answer if the question asked about the mechanism used to extract, transform, and load (ETL/ELT) data from source systems into a data warehouse or data lake, focusing on the flow and transformation of data rather than the storage architecture.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Data lakehouseCorrect answer▾
Why this is correct
A data lakehouse is the correct choice because it combines the cost-effective, schema-on-read flexibility of a data lake with the ACID transactions, indexing, and SQL analytics of a data warehouse. This unified architecture lets an enterprise store raw data in open formats (e.g., Parquet) while providing data reliability, time travel, and concurrency control often via Delta Lake, Apache Iceberg, or Hudi. It directly matches the requirement for raw storage plus analytical curation.
✗Data martWrong answer — click to see why▾
Why this is wrong here
A data mart is a subset of a data warehouse focused on a specific business domain, not a combined lake and warehouse architecture. The described architecture integrates data lake flexibility with warehouse ACID transactions, which is the definition of a data lakehouse.
★ When this WOULD be the correct answer
A question that asks: 'A sales department needs a dedicated, read-optimized dataset for reporting on regional sales, sourced from the enterprise data warehouse. Which component should they use?' The correct answer would be data mart.
Why candidates choose this
Candidates may confuse 'data mart' with 'data lakehouse' because both involve structured data for analytics, but they fail to recognize that a data mart lacks the raw data storage and schema-on-read flexibility of a lakehouse.
✗Operational databaseWrong answer — click to see why▾
Why this is wrong here
An operational database is designed for real-time transaction processing (OLTP), not for analytics. The question describes a read-time schema, curated ACID layer, and scalable repository for analytics, which is a data lakehouse, not an operational database.
★ When this WOULD be the correct answer
A question that asks: 'Which type of database is optimized for high-volume, low-latency transaction processing, such as order entry or banking transactions?' would have operational database as the correct answer.
Why candidates choose this
Candidates may confuse the ACID transactions mentioned in the curated layer with the ACID properties of operational databases, not realizing that data lakehouses also support ACID on data lakes.
✗Data pipelineWrong answer — click to see why▾
Why this is wrong here
A data pipeline is a process for moving and transforming data between systems, not an architecture that combines a data lake and data warehouse. The question describes a storage and processing architecture, not a data movement mechanism.
★ When this WOULD be the correct answer
A data pipeline would be the correct answer if the question asked about the mechanism used to extract, transform, and load (ETL/ELT) data from source systems into a data warehouse or data lake, focusing on the flow and transformation of data rather than the storage architecture.
Why candidates choose this
Candidates may confuse the concept of a data pipeline with the overall architecture because pipelines are essential for moving data into a lakehouse, leading them to incorrectly select this option as the architectural term.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Last reviewed: Jun 11, 2026
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