AZ-305 Design data storage solutions Practice Question
A company runs a custom analytics application that reads data using the NFS 3.0 protocol. The data consists of large files organized in a directory structure. The application also requires POSIX-like access control lists (ACLs) for fine-grained permissions. The solution must be fully managed and support high throughput for parallel reads. Which Azure data service should they use?
⚠ Common exam trap
Many candidates confuse Azure Files (which supports NFS but only version 4.1) with the NFS 3.0 requirement, or they overlook that Azure NetApp Files, while technically capable, is not the fully managed, high-throughput parallel read solution optimized for analytics that ADLS Gen2 provides.
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 and supports the NFS 3.0 protocol for high-throughput parallel reads. It is fully managed and designed for big data analytics workloads that require fine-grained permissions and directory structure management.
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 without the hierarchical namespace feature enabled is a flat object store, meaning blobs are addressed as key/value pairs in a single flat namespace and directory paths are only simulated by naming conventions rather than real directory entities. It does not provide POSIX ACLs or atomic, directory-level operations like rename or delete, and NFS 3.0 access is not available at all unless the hierarchical namespace is enabled (which turns it into ADLS Gen2). This breaks analytics applications that depend on POSIX semantics for file permissions and directory operations.
- ✗
Azure Files
Why it's wrong here
Azure Files is a managed file share service built around SMB and NFS 4.1 protocols, not NFS 3.0, so an application that specifically requires NFS 3.0 cannot mount it natively. Even if protocol mismatch were addressed, Azure Files is intended for general-purpose file shares such as lift-and-shift applications and user home directories, not for large-scale analytics workloads, and it lacks the Hadoop/Spark ABFS driver and elastic blob-backed data lake capabilities. Its access control model is tied to file-share mechanisms, not the POSIX ACL hierarchy expected across billions of analytical objects.
- ✗
Azure NetApp Files
Why it's wrong here
Azure NetApp Files does support NFS 3.0 and offers enterprise-grade file storage with low latency and snapshots, but it is fundamentally an all-flash, premium enterprise storage service, not a cloud-scale data lake. It is deployed in capacity pools with dedicated throughput tiers and entails premium per-GiB costs, and it does not integrate natively with Azure analytics tools via the ABFS driver the way ADLS Gen2 does. For a custom analytics application reading massive datasets, NetApp Files would require managing capacity pool scaling and cost profiles that do not align with blob-storage economics or elastic, exabyte-scale data lake patterns.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is the correct answer because it combines the massive scalability of Azure Blob Storage with a hierarchical namespace, enabling true directory structures and atomic, directory-level rename/delete operations that analytics applications require. It exposes POSIX-compliant access control lists (ACLs) and supports NFS 3.0 endpoints, so an NFS 3.0-based custom application can connect directly while also benefiting from the ABFS driver for Spark, Hadoop, and other analytic frameworks. This unique fusion of hierarchical namespace, POSIX ACLs, NFS 3.0 interoperability, and blob-storage economics makes ADLS Gen2 the only option that fully satisfies all the stated requirements for a cloud-scale analytics data lake.
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