AZ-204 Develop for Azure storage Practice Question
You need to implement a solution for storing and retrieving large amounts of unstructured data (e.g., images, videos, backups) in Azure, with high durability and availability. The solution must allow access from anywhere via HTTP/HTTPS and support both public and private access. Which TWO Azure storage services should you consider?
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Lake Storage Gen2 as a separate service, but it is actually built on top of Blob Storage, so both B and D are correct because they represent the same underlying technology for unstructured 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 Blob Storage is purpose-built for storing large amounts of unstructured data such as images, videos, and backups, offering high durability (99.9999999999% with RA-GRS) and availability. It supports access via HTTP/HTTPS from anywhere, and provides both public (anonymous) and private (SAS tokens, RBAC) access controls, making it the primary choice for this scenario.
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 Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store designed for structured, non-relational data. It stores entities with properties, making it suitable for datasets that require flexible schemas and fast access to specific rows. However, it is not designed for storing large binary objects or unstructured files like images, videos, or documents, which are typically handled by object storage solutions.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is the optimal choice for storing and retrieving large unstructured data, especially in big data analytics scenarios. Built upon Azure Blob Storage, it adds a hierarchical namespace and HDFS-compatible access, enabling petabyte-scale data lakes with high throughput and low latency. This makes it ideal for analytical workloads requiring POSIX-compliant file system semantics and integration with services like Azure Synapse Analytics and Azure Databricks.
- ✗
Azure Queue Storage
Why it's wrong here
Azure Queue Storage is a messaging service used for storing large numbers of messages that can be accessed from anywhere in the world via authenticated HTTP or HTTPS calls. Its primary purpose is to decouple application components and facilitate asynchronous communication, not for persistent storage and retrieval of large unstructured data files. Messages are typically transient, processed, and then deleted, and have size limitations, making it unsuitable for general data storage.
- ✓
Azure Blob Storage
Why this is correct
Azure Blob Storage is a highly scalable and cost-effective object storage solution specifically designed for storing massive amounts of unstructured data, such as images, videos, documents, and backups. It provides direct HTTP/HTTPS access to data objects (blobs) and supports various access tiers (Hot, Cool, Archive) to optimize costs based on access frequency. Its robust API and global availability make it a versatile choice for many unstructured data storage needs.
- ✗
Azure Files
Why it's wrong here
Azure Files provides fully managed file shares in the cloud that are accessible via the industry-standard Server Message Block (SMB) and Network File System (NFS) protocols. While it stores files, its primary use case is for traditional file system access for applications running on virtual machines or on-premises, rather than for petabyte-scale ingestion and processing of large, unstructured datasets for analytics. It is not optimized for the same high-throughput, low-latency demands of big data workloads as object storage solutions.
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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