DP-203 Design and implement data storage Practice Question
You are designing a data lake architecture using Azure Data Lake Storage Gen2. The data will be ingested from multiple sources with varying schemas. You need to organize the data in a way that supports both batch and streaming analytics while maintaining data lineage. Which folder structure convention should you use?
⚠ Common exam trap
Test-takers frequently choose Option B (source then date) because it seems logical for organization, but they overlook the requirement to support both batch and streaming analytics while maintaining data lineage, which the medallion architecture explicitly addresses through layered transformations.
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
✓
Use a medallion architecture with three layers: bronze (raw), silver (cleaned), gold (aggregated).
The medallion architecture (bronze, silver, gold) is the recommended pattern for Azure Data Lake Storage Gen2 when handling multiple sources with varying schemas. It supports both batch and streaming by storing raw data in bronze, applying incremental transformations in silver, and serving aggregated views in gold, while maintaining data lineage through clear layer boundaries and audit columns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Organize by ingestion date only, with subfolders for each source.
Why it's wrong here
Date-only organization lacks processing stage separation.
- ✗
Organize by source system, then by date.
Why it's wrong here
By source does not separate raw vs. processed data.
- ✓
Use a medallion architecture with three layers: bronze (raw), silver (cleaned), gold (aggregated).
Why this is correct
Medallion architecture provides clear separation and lineage.
- ✗
Organize by file format (CSV, Parquet, JSON) and date.
Why it's wrong here
File format organization does not reflect data transformation stages.
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