Question 464 of 161
AZ-305 Design data storage solutions Practice Question
A company wants to store raw data from IoT devices, social media feeds, and transactional databases for analytics. They need a storage solution that supports a hierarchical namespace for organizing data into directories and allows fine-grained access control at the directory and file level. They also need to query the data using Azure Synapse Analytics in-place. Which Azure storage solution should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Blob Storage (flat namespace, no directory ACLs) with ADLS Gen2 (hierarchical namespace, full ACL support) because both are built on the same underlying storage platform, but only ADLS Gen2 enables the directory-level organization and fine-grained access control required for enterprise analytics workloads.
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
✓
B
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it provides a hierarchical namespace that organizes data into directories and subdirectories, supports POSIX-like fine-grained access control at the directory and file level via ACLs, and can be queried in-place by Azure Synapse Analytics using its built-in serverless SQL pool or dedicated SQL pool. This combination of hierarchical namespace, granular security, and direct analytics integration makes it ideal for the described raw data storage and analytics 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.
- ✗
A
Why it's wrong here
Standard Azure Blob Storage lacks a hierarchical namespace, meaning it treats data as a flat object store rather than providing directory-level access control via Microsoft Entra ID. This makes it impossible to perform the fine-grained file-level permissions required by the scenario. It is tempting because this service provides low-cost storage for unstructured data and serves as an ideal target for simple, high-volume data lakes where complex folder hierarchies are unnecessary.
- ✓
B
Why this is correct
Azure Data Lake Storage Gen2 is Azure Blob Storage with a hierarchical namespace, enabling directory-level ACLs that integrate with Microsoft Entra ID for fine-grained permission control. It is optimized for large-scale analytics workloads, supports POSIX-like permissions, and integrates natively with Azure Synapse Analytics for in-place querying without data movement. Its ability to organize data into directories and subdirectories while remaining accessible via both Blob and ADLS Gen2 APIs makes it the ideal landing zone for structured and unstructured IoT and social media data.
- ✗
C
Why it's wrong here
Azure Files provides fully managed SMB/NFS file shares meant for lift-and-shift file server migrations or sharing files between VMs and on-premises, not for parallel analytics engines. It lacks a hierarchical namespace for fine-grained directory ACLs (only share-level and file-level permissions at limited granularity) and does not support native integration with Azure Synapse or Spark. Additionally, its performance and throughput are designed for I/O- and latency-sensitive workloads rather than high-throughput analytical scans of massive IoT data sets.
- ✗
D
Why it's wrong here
Azure Disk Storage provides raw block-level storage attached to a single virtual machine, offering neither shared multi-engine access nor a hierarchical namespace for directory-level permissions. Unlike object storage, a disk cannot be simultaneously mounted to multiple analytics compute instances, and any data stored there must be copied into another service before distributed analytics engines can query it. This makes Disks suitable for OS/filesystem workloads but incompatible with a centralized, concurrently accessed data lake.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 11, 2026
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.
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