AZ-305 Design data storage solutions Practice Question
A company needs to store large amounts of unstructured data such as images and videos for a content management system. The data must be accessible via HTTPS and support tiered storage for cost optimization. Which Azure service should they use?
⚠ Common exam trap
Many candidates confuse Azure Data Lake Storage (which is built on Blob Storage) as a separate service for unstructured data, but it is specifically optimized for analytics workloads, not general-purpose content management with tiered storage.
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 Blob Storage
Azure Blob Storage is the correct choice because it is designed for storing large amounts of unstructured data (such as images and videos) and provides HTTPS access. It also offers tiered storage (hot, cool, cold, and archive tiers) to optimize costs based on data access patterns, making it ideal for a content management system.
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 Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed, multi-model NoSQL database service that stores data as JSON documents, graphic relationships, or key-value pairs, not as raw binary blobs or files. It is optimized for low-latency transactional queries and schema-flexible application data, not for the high-throughput ingestion and retrieval of large, opaque unstructured objects like images, videos, or backups. Using Cosmos DB for object storage would waste its indexing and consistency features while not providing the simple REST-based blob access pattern that unstructured data typically requires.
- ✓
Azure Blob Storage
Why this is correct
Azure Blob Storage is Microsoft's object storage solution, purpose-built for storing massive amounts of unstructured data—anything from text and binary streams to images, logs, and application backups. It exposes a flat namespace via REST over HTTPS, supports data tiering to hot/cool/archive for cost optimization, and provides life-cycle management, soft-delete, and replication options that meet enterprise durability and disaster-recovery requirements. Because the requirement explicitly calls for large-scale unstructured data and HTTPS access, Blob Storage is the direct, default Azure service for that scenario.
- ✗
Azure Data Lake Storage
Why it's wrong here
Azure Data Lake Storage Gen2 is not a separate storage service; it is Blob Storage enhanced with a hierarchical namespace and POSIX-compliant access control lists, designed specifically for big-data analytics and high-throughput parallel processing by frameworks like Spark and Hive. While it can store unstructured data, its additional complexity—namespace organization, file-level ACLs, and analytics-oriented optimization—provides little benefit when the goal is general-purpose object storage or content management. For a generic requirement to store large amounts of unstructured data without a defined analytics workload, the standard Blob Storage abstraction remains the appropriate choice.
- ✗
Azure Files
Why it's wrong here
Azure Files offers fully managed cloud file shares that are accessed over SMB or NFS protocols, intended for scenarios where you need to mount a shared file system on multiple VMs or on-premises clients. It supports features like Active Directory integration and Azure File Sync, but it is not an object-store with REST-based HTTPS access; instead, it presents a traditional directory tree, which can introduce protocol and performance limitations when storing vast numbers of small unstructured objects. The requirement's emphasis on HTTPS access and unstructured data points away from file shares and toward Blob Storage.
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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