AZ-305 Design data storage solutions Practice Question
An enterprise data platform must store petabytes of raw files for analytics and support fine-grained access control through Microsoft Entra ID. Which storage solution should be selected?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Blob Storage (which lacks a hierarchical namespace and fine-grained ACLs) with ADLS Gen2, or assume that any Azure storage service can handle petabyte-scale analytics, ignoring the specific requirements for Entra ID integration and granular permissions.
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 a hierarchical namespace with Azure Blob Storage, enabling petabyte-scale storage for raw files and fine-grained access control via POSIX-like ACLs integrated with Microsoft Entra ID (formerly Azure AD). This makes it the ideal solution for enterprise analytics requiring both massive capacity and granular security.
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 Queue Storage
Why it's wrong here
Azure Queue Storage is a messaging service designed for asynchronous communication and decoupling application components, not for storing analytical data. Each message is limited to 64 KB, and the service lacks a hierarchical namespace, file-system semantics, and the ability to support petabyte-scale query jobs or data lake access patterns. A petabyte-scale raw data store requires durable, tiered object storage with concurrent, high-throughput analytics access, which Queue Storage does not provide.
- ✗
Azure Disk Storage attached to one VM
Why it's wrong here
Azure Disk Storage is block-level storage attached to a single virtual machine, so only that one VM can access the disk at a time, preventing the concurrent multi-worker reads and writes typical of analytics workloads. Even the largest managed disk is capped at 32 TiB, which is orders of magnitude below petabyte capacity, and its IOPS/throughput is scaled by the VM instance, not horizontally across a shared storage fabric. It also lacks the hierarchical namespace and directory-level security that an enterprise data lake requires.
- ✗
Azure Cache for Redis
Why it's wrong here
Azure Cache for Redis is an in-memory key-value store meant for low-latency caching, session state, and transient data, not for durable persistence of raw enterprise data. Its total capacity is limited by server memory (typically tens of gigabytes to a few hundred GB per cache instance, even in Enterprise tier), far less than petabytes, and because it is volatile it provides no data durability guarantee against restarts or failures. Analytics workloads need persistent, schema-on-read data storage, not an in-memory cache that must be reloaded and cannot support large-scale scan operations.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines Blob Storage's virtually unlimited scalability and durability with a hierarchical namespace, enabling directory-level rename and atomic operations. It integrates with Entra ID to enforce POSIX-like ACLs and RBAC, giving fine-grained, identity-based security over raw petabytes, and it is natively supported by analytics engines such as Apache Spark, Hive, and Azure Synapse. This makes it a true data lake capable of storing raw enterprise data at any volume while providing high-throughput access.
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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