AZ-305 Design data storage solutions Practice Question
You are designing a data lake for advanced analytics in Azure. The data includes structured, semi-structured, and unstructured data. The solution must support schema-on-read and have the ability to query using SQL. Which Azure service should you choose?
⚠ Common exam trap
Test-takers frequently confuse Azure Blob Storage's object storage capabilities with the hierarchical namespace and SQL query support of ADLS Gen2, or they mistakenly choose Azure SQL Database for its SQL familiarity without recognizing it requires a predefined schema.
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) combines Blob Storage with a hierarchical namespace, enabling schema-on-read for structured, semi-structured, and unstructured data. It supports SQL-based querying via Azure Synapse Analytics, Azure Databricks, or PolyBase, making it ideal for advanced analytics scenarios.
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 object storage that lacks a hierarchical namespace, meaning it cannot support atomic directory-level operations like rename or delete, and it does not expose POSIX permissions. Although it can store any file type, this absence of file system semantics makes it poorly suited for the high-throughput, path-based access patterns that analytical engines and pipelines require from a data lake, so it is not the ideal choice.
- ✗
Azure SQL Database.
Why it's wrong here
Azure SQL Database is a fully managed relational database that enforces a rigid schema-on-write model, requiring data to be structured and precisely typed before any ingestion. This contradicts the core principle of a data lake—schema-on-read—where raw and semi-structured or unstructured data (e.g., JSON, Parquet, CSV, logs, images) must be stored as-is without transformation. Its fixed relational schema and scale-up architecture also make it inefficient for storing the massive, heterogeneous volumes of data typical of advanced analytics workloads.
- ✓
Azure Data Lake Storage Gen2.
Why this is correct
Azure Data Lake Storage Gen2 (ADLS Gen2) is built on Azure Blob Storage but adds a hierarchical namespace, giving it file system semantics such as atomic directory renames and POSIX-like access control lists. It supports every data type and applies schema-on-read, meaning raw data is ingested without ETL and can be queried on demand by engines like Azure Synapse, Databricks, and PolyBase. Its native integration with SQL querying, including serverless SQL pools, makes it the optimal foundation for a scalable, high-performance data lake for advanced analytics.
- ✗
Azure Cosmos DB.
Why it's wrong here
Azure Cosmos DB is a globally distributed, multi-model NoSQL database engineered for low-latency transactional access to documents, key-value pairs, graphs, and columnar data. It does not expose a file-based or hierarchical namespace interface, and its throughput-provisioned pricing and storage model are impractical for storing raw analytical data at petabyte scale. While Cosmos DB can serve as a real-time serving layer alongside a data lake, it is not designed to be the primary storage engine for a data lake.
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