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Design data storage solutionshardMultiple ChoiceObjective-mapped

AZ-305 Design data storage solutions Practice Question

A company runs large-scale analytics workloads using Apache Hadoop and Spark. They need a cloud storage solution that is fully compatible with the Hadoop Distributed File System (HDFS) and provides unlimited storage with high throughput for parallel processing. They also want to take advantage of tiered storage to reduce costs for older data. Which Azure data service should they use?

⚠ Common exam trap

Candidates often confuse Azure Blob Storage (which is object storage without a hierarchical namespace) with ADLS Gen2, assuming both are equally HDFS-compatible, but only ADLS Gen2 provides the required HDFS semantics and the `abfss://` driver for native Hadoop/Spark integration.

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 Azure Blob Storage, providing full HDFS compatibility. This allows Apache Hadoop and Spark workloads to use the `wasbs://` or `abfss://` driver for unlimited storage and high throughput parallel processing, while also supporting tiered storage (hot, cool, archive) to reduce costs for older data.

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 is a flat object store with no native hierarchical namespace. Hadoop can access it only through the legacy wasb driver, which emulates directories but lacks atomic rename and HDFS ACL semantics. For large-scale analytics, this adds latency, performs poorly under rename-heavy jobs, and cannot enforce Apache Ranger or POSIX permissions.

  • Azure Data Lake Storage Gen2

    Why this is correct

    Azure Data Lake Storage Gen2 is the correct choice because it merges Blob Storage's durable object storage with a hierarchical namespace and native HDFS support via the ABFS driver. It provides unlimited storage, POSIX-like permissions, atomic directory renames, and high throughput, enabling Spark and Hadoop jobs to run at scale without a dedicated HDFS cluster. Lifecycle tiering further reduces costs while retaining analytics performance.

  • Azure Files

    Why it's wrong here

    Azure Files offers SMB and NFS file shares intended for lift-and-shift applications and stateful files, but it is not HDFS compatible and does not expose a hierarchical namespace for Hadoop. Its throughput and IOPS limits are optimized for single-tenant file access, not parallel high-throughput analytics, and it lacks a native ABFS or wasb interface.

  • Azure Disk Storage

    Why it's wrong here

    Azure Disk Storage provides block-level volumes attached to a single VM or VM scale set, with per-disk capacity caps and IOPS limits. To reach large scale you'd have to manually stripe many disks, and those disks are not inherently shared across analytics nodes without premium file share on top. This creates a bottleneck, higher management overhead, and no built-in HDFS semantics for distributed data processing.

Quick reference

Azure Blob Storage Tier Comparison

TierStorage CostRetrieval CostLatencyUse Case
HotHighestLowestImmediateActive data, frequent reads
CoolLowerHigherImmediateData accessed < once / month
ColdLower stillHigherImmediateData accessed < once / quarter
ArchiveLowestHighest + rehydration delayHoursLong-term compliance retention

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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