Question 776 of 161
AZ-305 Design data storage solutions Practice Question
A company is building a petabyte-scale data lake for analytics. The workload includes Apache Spark and Hive jobs that read and write large files. The storage solution must support a hierarchical namespace for efficient directory operations, POSIX-like access control lists (ACLs) for fine-grained permissions, and must be accessible via the Azure Blob Storage API for compatibility with existing tools. Furthermore, the solution should be optimized for analytics workloads with high throughput. Which Azure data service should they choose?
⚠ Common exam trap
It's easy for candidates to confuse Azure Data Lake Storage Gen1 with Gen2, overlooking that Gen1 lacks Blob Storage API compatibility, or they may assume Azure Blob Storage with hierarchical namespace enabled is a separate service, but ADLS Gen2 is the specific offering that combines all required features.
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 accessible via the Azure Blob Storage API. This service is specifically optimized for analytics workloads like Apache Spark and Hive, providing high throughput for petabyte-scale data lakes. The hierarchical namespace enables efficient directory operations, while the Blob Storage API ensures compatibility with existing tools.
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 Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is correct because it layers a hierarchical namespace onto Blob Storage, providing POSIX ACLs, atomic directory renaming, and a Hadoop-compatible `abfs://` filesystem that Spark, Hive, and Presto can use directly. It also fully supports the Blob API and Azure SDKs, so existing tooling works unchanged, while delivering the scale, encryption, and lifecycle policies needed to run petabyte-scale analytics workloads.
- ✗
Azure Data Lake Storage Gen1
Why it's wrong here
Azure Data Lake Storage Gen1 is wrong because it is a legacy service that Microsoft has retired, and it is built on the WebHDFS API rather than the Blob API, making it unable to integrate with the broader Azure Blob ecosystem such as Event Grid, Azure Functions, and modern SDKs. Although Gen1 offers a hierarchical namespace and POSIX ACLs, it lacks native support for features like managed-file encryption via Azure Storage service keys and seamless Azure Synapse and Databricks integration, so customers must migrate to ADLS Gen2.
- ✗
Azure Blob Storage
Why it's wrong here
Standard Azure Blob Storage without the hierarchical namespace feature is a flat object store where directories are only virtual prefixes on blob names, so operations like renaming a folder are implemented as costly copy-and-delete loops and are not atomic. It also does not natively enforce POSIX ACLs or data-lake-style permission checks, which forces analytics engines to perform manual prefix scans and breaks the atomic directory semantics that Spark and Hive jobs rely on at petabyte scale; enabling the hierarchical namespace on the account is what turns it into ADLS Gen2.
- ✗
Azure Files
Why it's wrong here
Azure Files is a managed SMB/NFS file share service designed for lift-and-shift file servers, shared application data, and user home directories, not for high-throughput analytics with Spark or Hive. It does not expose the Blob API or provide a Hadoop filesystem connector with atomic directory rename semantics, and a single Azure Files share is capped at 100 TiB, forcing you to shard data manually across many shares to reach petabyte scale, which is impractical and inefficient for this workload.
Visual reference
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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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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