DP-900 Describe an analytics workload on Azure Practice Question
A company is designing a data analytics solution. They need to store large volumes of raw data in its native format and support schema-on-read for data science exploration. Which storage technology should they use?
⚠ Common exam trap
Many candidates confuse Azure Blob Storage with ADLS Gen2 because both store objects, but Blob Storage lacks the hierarchical namespace and native schema-on-read support required for data science exploration, making it unsuitable for this specific analytics workload.
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, allowing raw data to be stored in its native format (e.g., CSV, JSON, Parquet) without transformation. It supports schema-on-read, meaning the schema is applied at query time (e.g., via Apache Spark or Azure Synapse SQL), which is ideal for data science exploration where the data structure may not be predefined.
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
Azure Data Lake Storage Gen2 is a purpose-built data lake that combines Blob Storage's low-cost object storage with a hierarchical namespace, enabling efficient directory-level operations and POSIX-compliant access control. It supports schema-on-read, so raw data in any format (JSON, CSV, Parquet, etc.) can be ingested without transformation, and it natively integrates with Azure Synapse, Databricks, and Data Factory for analytics workloads.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is a general-purpose object store that handles unstructured data but lacks the hierarchical namespace and ACID metadata capabilities of ADLS Gen2. While it can hold raw files, its flat namespace and limited analytics integration make it less suitable for big data processing that requires folder-level security and high-throughput parallel access from analytics services.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a multi-model, globally distributed NoSQL database designed for low-latency transactional (OLTP) workloads, not for data lake storage. It requires data to be written as documents or graphs with defined logical schemas, making it unsuitable for storing unfiltered raw files in varied formats for later schema-on-read analytics.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational engine that enforces strict schema-on-write, requiring every table's columns and data types to be defined before data is loaded. This rigidity conflicts with the need to store raw, loosely structured data, and its transactional performance optimization makes it far more expensive — and ill-suited — as a landing zone for big data analytics compared to a proper data lake.
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
Data Roles and Core Concepts
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
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