DP-900 Describe an analytics workload on Azure Practice Question
A company wants to build a data lake on Azure for storing structured, semi-structured, and unstructured data. The solution must support fast queries on structured data without moving data to a separate store. Which Azure service should they use?
⚠ Common exam trap
The trap here is that candidates often pick Azure Blob Storage because it is the underlying storage for ADLS Gen2, but they miss that ADLS Gen2's hierarchical namespace is required for fast, directory-level queries and direct SQL access without moving data.
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, enabling both a data lake for unstructured and semi-structured data and fast, SQL-like queries on structured data using Azure Synapse Analytics or PolyBase without moving data. It supports the ABFS (Azure Blob File System) driver for high-performance analytics and integrates directly with query engines like Apache Spark and Azure Synapse SQL.
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 Blob Storage
Why it's wrong here
Azure Blob Storage is a general-purpose object storage service, but it lacks the hierarchical namespace that provides true file-system semantics such as directories and atomic rename operations. Without a hierarchical namespace, because blob storage treats data as flat blobs, analytics engines like Hadoop and Spark cannot efficiently partition data or manage permissions using POSIX-compliant access control lists. This makes it less suitable as a dedicated big-data lake platform despite being the underlying raw storage technology.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational database management system built for online transaction processing (OLTP) with a fixed schema, and it is not designed to store raw, varied file formats such as JSON, Parquet, or CSV at massive scale. A data lake requires a storage layer that accepts any data shape and defers schema definition until query time, whereas SQL Database enforces strict schema constraints and requires data to be normalized. Attempting to use it for a data lake would incur prohibitive mapping costs and lose the flexibility needed for exploratory big-data analytics.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is the correct choice because it combines the scalability and cost efficiency of Azure Blob Storage with a hierarchical namespace and POSIX-compliant access controls, making it a dedicated big-data analytics storage platform. It provides atomic directory operations, security at the file and folder level, and high-throughput connectivity from analytics services such as Azure Synapse Analytics, HDInsight, and Databricks. This enables storage of trillions of files and exabytes of data while preserving the performant file-system semantics required for data lake workloads.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed, multi-model NoSQL database optimized for low-latency transactional applications, not for centralizing raw analytic data. It stores data as documents, key-value pairs, graphs, or wide columns, and it is not a file system, so it cannot hold arbitrary blobs or flat files like a data lake. Using Cosmos DB would complicate analytic pipelines because it lacks the bulk file ingestion and schema-on-read capabilities that a data lake demands.
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
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.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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