AZ-305 Design data storage solutions Practice Question
A data platform must support analytical queries over petabytes of files in a data lake, while preserving hierarchical namespaces and fine-grained ACLs. Which storage service should you design around?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Files (which also supports ACLs) with ADLS Gen2, overlooking that Azure Files is optimized for shared file access (SMB/NFS) and not for petabyte-scale analytical data lake workloads with hierarchical namespace and POSIX ACLs.
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 POSIX-like ACLs, enabling fine-grained access control at the file and directory level while supporting petabyte-scale analytical workloads. It is built on Azure Blob Storage, providing high-throughput and parallel processing for big data analytics engines like Azure Synapse, Spark, and Hadoop.
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 correct because it combines Azure Blob Storage's durable, scalable object storage with a hierarchical namespace, enabling true file/directory semantics such as atomic rename and POSIX-like access control lists (ACLs). It is specifically engineered as a scalable data lake for petabyte-scale analytical workloads, supporting parallel-processing engines like Azure Synapse, Databricks, and Hadoop via ABFS (Azure Blob File System) driver. This design optimizes throughput for full-scan analytical queries and allows incremental directory-level operations, making it the default storage platform for enterprise data platforms.
- ✗
Azure Files premium shares.
Why it's wrong here
Azure Files premium shares are wrong because Azure Files is a managed SMB/NFS file share service intended for lifting on-premises file servers or hosting containerized apps, not for petabyte-scale analytical lakehouses. Premium shares offer high IOPS and low latency but are capped in total capacity and per-share throughput, making them cost-prohibitive and technically unsuitable for massive parallel analytical scans. Additionally, they rely on SMB/NFS protocol overhead rather than a cloud-native distributed file system, so they lack the hierarchical namespace performance optimizations and ecosystem integrations needed for big data engines like Spark or Presto.
- ✗
Azure Table Storage.
Why it's wrong here
Azure Table Storage is wrong because it is a schemaless NoSQL key-value store optimized for point lookups, small range queries, and rapid reads/writes of web-scale structured data, not for analytical petabyte-scale warehouses. It provides no query engine for complex aggregations, joins, or columnar scans, and has a maximum row/timeout model that cannot sustain full-table analytics on large datasets. It also lacks hierarchical directory semantics and ACLs for data lake governance, so it is fundamentally a service for operational data, not for supporting analytical queries over petabytes.
- ✗
Azure Queue Storage.
Why it's wrong here
Azure Queue Storage is wrong because it is a cloud message queue service used for asynchronous decoupling of application components, not a storage system designed for analytical data. Messages are transient, time-limited (up to 7 days), and capped at 64 KB each, with no query language or indexing for data exploration. It provides no durable, scalable file/blob abstractions or analytical processing capabilities; therefore, it cannot support petabyte-scale analytical queries and is entirely unrelated to data platform storage needs.
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 |
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