DP-203 Design and implement data storage Practice Question
You need to design a data storage solution for a batch processing pipeline that processes petabytes of data daily. The data is stored in Parquet format and must be accessible by both Azure Databricks and Azure Synapse Analytics. Which storage solution should you recommend?
⚠ Common exam trap
Many candidates confuse Azure Blob Storage (flat namespace) with ADLS Gen2 (hierarchical namespace), assuming both are equivalent for big data analytics, but the hierarchical namespace is a critical differentiator for performance at petabyte scale in batch pipelines.
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
✓
Azure Data Lake Storage Gen2
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing native POSIX-like access control and high throughput for petabyte-scale batch processing. Both Azure Databricks and Azure Synapse Analytics have optimized connectors for ADLS Gen2 that leverage the hierarchical namespace for efficient partition pruning and file listing, which is critical for Parquet-based analytics at this scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Provides hierarchical namespace, high throughput, and is compatible with Databricks and Synapse.
- ✗
Azure Files
Why it's wrong here
Azure Files is for SMB file shares, not analytics.
- ✗
Azure SQL Database
Why it's wrong here
Not designed for petabyte-scale storage.
- ✗
Azure Blob Storage
Why it's wrong here
Blob Storage is less performant for analytics workloads compared to ADLS Gen2.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Key term
Azure Data Lake Gen2
Azure Data Lake Gen2 is a cloud-based data storage service that combines the scalability and performance of a data lake with the hierarchical file system and security of a data warehouse, designed for big data analytics.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.