AZ-305 Design data storage solutions Practice Question
A company is building a petabyte-scale data lake for analytics. They need a storage solution that supports a hierarchical namespace, POSIX-like permissions (ACLs), and is optimized for big data analytics workloads using Apache Spark and Hive. The data must be accessible over the Azure Blob Storage API. Which Azure data service should they use?
⚠ Common exam trap
Many exam-takers confuse Azure Blob Storage (which is object storage with a flat namespace) with ADLS Gen2, not realizing that the hierarchical namespace and POSIX ACLs are exclusive to ADLS Gen2 and critical for big data analytics engines like Spark and Hive.
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 is natively optimized for big data analytics workloads like Apache Spark and Hive. It exposes data through the Azure Blob Storage API, meeting all stated requirements for petabyte-scale analytics.
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 (with flat namespace)
Why it's wrong here
Azure Blob Storage with a flat namespace lacks the hierarchical namespace required for POSIX-like ACLs and efficient directory-level operations in Apache Spark and Hive. It is tempting because it natively supports the Azure Blob Storage API and is commonly used for general-purpose object storage, but it would be the correct choice only when a flat namespace suffices and hierarchical access controls are unnecessary.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is the correct choice because it provides a hierarchical namespace atop Blob Storage, enabling POSIX-like ACLs and directory-level operations that Apache Spark and Hive require for efficient metadata-heavy workloads. It offers Hadoop-compatible access (ABFS driver) natively, supports petabyte-scale analytics, and retains Blob Storage API compatibility for existing applications. Unlike a flat namespace, the hierarchical namespace allows atomic, rename-based directory operations and fine-grained security, which are critical for high-throughput analytics pipelines exploring partition pruning and O(1) directory scans.
- ✗
Azure NetApp Files
Why it's wrong here
Azure NetApp Files is a fully managed, enterprise-grade NFS/SMB file share service, primarily designed for latency-sensitive workloads like VDI, HPC, and database files, not for petabyte-scale analytical data lakes. It does not expose a Blob API or Hadoop-compatible file system (no ABFS), so Spark and Hive cannot directly leverage its performance characteristics via standard connectors. While it offers strong consistency and NFS semantics, its cost per terabyte and limited namespace scalability make it unsuitable for archiving or analytical scanning at the scale and concurrency typically required by a data lake.
- ✗
Azure HPC Cache
Why it's wrong here
Azure HPC Cache is a caching service that accelerates access to data stored in other backends (such as ADLS Gen2, NetApp Files, or Blob Storage) by maintaining a hot tier of frequently accessed files for high-performance computing clusters. It is not a primary storage solution and does not persist data independently; it lacks a native namespace with ACLs or metadata that analytics engines like Spark and Hive can directly query for cataloging. Deploying it as the primary storage for a data lake would introduce an unnecessary intermediary for persistence, and because its design targets bursty HPC workloads rather than sustained analytical scans, it cannot serve as the underlying petabyte-scale repository.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-305 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-305 exam.