DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure using Azure Data Lake Storage Gen2 as the storage layer. You need to ensure that data ingested from various sources is immutable and can be used for both batch and streaming workloads. Which storage design pattern should you implement?
⚠ Common exam trap
A common mix-up: candidates confuse data modeling patterns (star schema, data vault) with storage layer design patterns, assuming any structured approach ensures immutability, when in fact only the medallion architecture explicitly separates raw immutable storage from refined layers for batch and streaming workloads.
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
✓
Implement a medallion architecture with bronze, silver, and gold layers.
The medallion architecture (bronze, silver, gold) is the correct pattern because it enforces immutability at the bronze layer (raw ingested data is never modified), while providing progressively refined, query-optimized views for both batch and streaming workloads in Azure Data Lake Storage Gen2. This design supports schema-on-read, enables reprocessing from raw data, and aligns with lakehouse principles for unified analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store data in a normalized relational database structure.
Why it's wrong here
A normalized relational structure imposes mutable row-level updates and schema-on-write, so it cannot store immutable raw files or serve streaming and batch readers from the same Data Lake Gen2 path. It is tempting because normalisation reduces redundancy in transactional systems, which is the wrong workload here.
- ✓
Implement a medallion architecture with bronze, silver, and gold layers.
Why this is correct
The medallion pattern layers bronze raw, silver cleansed and gold curated data in ADLS Gen2. Bronze preserves ingested data immutably for replay, while silver and gold serve batch and streaming consumers, meeting the immutability and dual-workload constraints.
- ✗
Use a data vault model with hubs, links, and satellites.
Why it's wrong here
Data vault modelling structures historical, auditable integration layers in a relational warehouse; it does not itself provide immutable file storage or a single layout readable by both batch and streaming engines on Data Lake Gen2. It is tempting because data vaults preserve full history, which resembles immutability but operates at table level.
- ✗
Design a star schema with fact and dimension tables.
Why it's wrong here
A star schema organises curated data for analytical query performance; it neither enforces immutability on ingestion nor provides the append-only, zone-structured layout that batch and streaming consumers both read. It is tempting because star schemas are the standard dimensional modelling pattern for a serving layer once data has already been transformed.
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