DP-900 Describe an analytics workload on Azure Practice Question
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
⚠ Common exam trap
Many candidates confuse the medallion architecture's sequential data lake zones with Lambda architecture's parallel batch/stream processing layers, or incorrectly associate star/snowflake schemas with data lake layering instead of data warehouse modeling.
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
✓
Medallion architecture
The medallion architecture (bronze, silver, gold) is a layered data lake design pattern where raw data lands in the bronze zone, is cleaned and deduplicated in the silver zone, and aggregated into business-ready datasets in the gold zone. This directly matches the described three-zone ingestion, transformation, and aggregation pipeline in Azure Data Lake Storage Gen2.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Star schema
Why it's wrong here
Star schema is a dimensional modeling technique used in data warehouses, organizing data into fact tables for measures and dimension tables for descriptive attributes. It optimizes query performance for BI reporting by denormalizing dimensions, but it does not describe how raw files are ingested, refined, or layered in a data lake. While a star schema might be applied on top of curated data in a serving layer, it is not an architecture for staging or transforming data at different quality levels.
- ✗
Snowflake schema
Why it's wrong here
Snowflake schema is a refinement of the star schema where dimension tables are normalized into multiple related tables, reducing redundancy at the cost of more joins. This is a data warehouse physical or logical design choice for relational analytics, not a data lake layering strategy. A data lake architecture question about bronze, silver, and gold layers is concerned with data refinement stages and storage organization, whereas snowflake schema is about table shapes and relationship granularity, making it inapplicable here.
- ✓
Medallion architecture
Why this is correct
Medallion architecture is the correct pattern because it incrementally improves data quality across bronze, silver, and gold layers. Bronze stores raw ingested data as-is, silver applies validation and cleansing and conforms schemas, and gold provides business-ready, aggregated datasets for reporting and machine learning. This layered approach is standard for lakehouse implementations on Azure, for example Azure Data Lake Storage with Delta Lake, and directly matches the goal of designing a data lake architecture.
- ✗
Lambda architecture
Why it's wrong here
Lambda architecture is a streaming-centric pattern that maintains two parallel pipelines for the same data: a batch layer for accurate historical processing and a speed layer for low-latency, near-real-time approximations. It does not prescribe quality tiers like bronze, silver, and gold, and it focuses on handling event-time versus processing-time discrepancies rather than organizing data lake storage into progressively refined zones. Therefore, while valid for real-time analytics, it is not the right answer for a layered data lake data-quality design.
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Related to this question
Learn chapter
Data Roles and Core Concepts
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.
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
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