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DP-900 Practice Question: Describe considerations for working with non-relational data on Azure

A healthcare organization needs to store large volumes of unstructured patient records, including PDFs and images, in Azure. They require the ability to query these records based on metadata such as patient ID and record type. They also want to minimize cost for long-term storage. Which Azure service should they use?

⚠ Common exam trap

The trap here is assuming that a database service like Azure Cosmos DB or SQL Database is needed to query metadata, when Blob Storage with index tags provides a cost-effective and scalable solution for unstructured data.

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 Blob Storage with blob index tags

Azure Blob Storage with blob index tags is the best choice because it efficiently stores large unstructured files, supports metadata-based querying through index tags, and offers cost-effective storage tiers for long-term retention. This combination meets the requirements for storing and querying patient records while minimizing costs.

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 Cosmos DB for NoSQL

    Why it's wrong here

    Azure Cosmos DB is designed for structured and semi-structured data as JSON documents, not for large binary files like PDFs and images. While you can store binary data as Base64 strings, it is inefficient and costly for large volumes. It also lacks native support for cost-effective long-term storage tiers, making it unsuitable for this scenario.

  • ✓

    Azure Blob Storage with blob index tags

    Why this is correct

    Azure Blob Storage is ideal for storing large unstructured files like PDFs and images. Blob index tags allow you to categorize and query blobs using key-value tags, enabling efficient metadata-based searches without moving data. It also supports cost-effective tiering, such as moving data to cool or archive tiers for long-term storage, minimizing costs.

  • ✗

    Azure SQL Database with FileTable

    Why it's wrong here

    Azure SQL Database with FileTable allows storing files in a relational database, but it is not cost-effective for large volumes of unstructured data. It also lacks the scalability and cost-tiering options of Blob Storage. Additionally, querying metadata would require SQL queries, which may be less efficient than blob index tags for this purpose.

  • ✗

    Azure Data Lake Storage Gen2

    Why it's wrong here

    Azure Data Lake Storage Gen2 is optimized for big data analytics with hierarchical namespace, but it is not specifically designed for querying metadata of individual files like PDFs and images. While you can store files there, querying based on metadata would require additional services like Azure Synapse or Databricks, increasing complexity and cost.

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 and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Microsoft exam blueprint

This DP-900 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 DP-900 exam.