Describe considerations for working with non-relational data on Azure →mediumMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A media company stores large video files and associated metadata (title, duration, tags) as JSON documents. The application requires low-latency streaming of videos to users worldwide and the ability to quickly query metadata by tag. Which combination of Azure services should the company use?
⚠ Common exam trap
Candidates often assume a single service (like Blob Storage or Cosmos DB) can handle both data types, but the exam tests understanding that each Azure service has specific strengths—blobs for large binary objects and Cosmos DB for low-latency document queries—and that mixing them is the correct architectural pattern.
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 for videos and Azure Cosmos DB for metadata
Azure Blob Storage is optimized for storing large binary objects like video files, offering high-throughput streaming via HTTP/HTTPS and integration with CDN for low-latency global delivery. Azure Cosmos DB provides single-digit millisecond read and write latencies with automatic indexing, making it ideal for quickly querying JSON metadata by tag using SQL or MongoDB API. This combination separates storage concerns (blobs for raw video, document DB for structured metadata) to meet both streaming and query performance requirements.
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 for videos and Azure Cosmos DB for metadata
Why this is correct
Azure Blob Storage is purpose-built for large unstructured binary data: it offers high-throughput write/read, configurable access tiers, and HTTPS-based access suitable for storing and delivering video files at scale. Azure Cosmos DB complements this by storing video metadata as flexible JSON documents, with automatic indexing and sub-millisecond point reads that support rich queries on tags, durations, and upload dates. This pairing keeps the media payload and its searchable catalog decoupled, so storage optimization and query performance are each handled by the most appropriate service.
- ✗
Azure Blob Storage for both videos and metadata
Why it's wrong here
While Azure Blob Storage can indeed hold both video files and JSON metadata blobs, it does not provide native query capabilities over arbitrary blob content: retrieving metadata would require listing and downloading blobs, then filtering client-side, which scales poorly. Blob index tags exist but support only exact key-value matches on blob-level attributes, not the rich, nested JSON queries needed for media metadata like genres, subtitles, or version history. Keeping metadata in blobs couples the catalog format to an unqueryable storage engine, forcing an additional indexing layer to make the data usable.
When this WOULD be correct
A company stores large video files and associated metadata as JSON documents, but the application only needs to stream videos and does not require any querying of metadata. In that case, Azure Blob Storage for both videos and metadata would be sufficient.
- ✗
Azure Cosmos DB for videos and Azure Table Storage for metadata
Why it's wrong here
Storing videos as documents in Azure Cosmos DB is impractical because each document is limited to 2 MB, and video files routinely far exceed that single-item constraint, forcing fragmentation and degrading performance. On the metadata side, Azure Table Storage is a key-value store that requires a predefined partition key and row key, and its query capabilities are limited to exact matches on indexed keys or simple range scans, not flexible filtering over nested JSON tags. Thus the combination fails both the size requirements for media and the ad-hoc querying needed for rich metadata.
When this WOULD be correct
If the question asked for storing small media files (e.g., thumbnails) with metadata that requires global distribution and low-latency queries, and the videos were stored elsewhere, then Cosmos DB for media and Table Storage for metadata could be correct.
- ✗
Azure Files for videos and Azure SQL Database for metadata
Why it's wrong here
Azure Files provides SMB/NFS file shares intended for lift-and-shift file access, not as a globally distributed streaming origin; it lacks Blob Storage's massive scale, cost-efficient hot/cool tiers, and native CDN/streaming integrations. Azure SQL Database imposes a fixed relational schema, so storing variable-length JSON tags would require either an unwieldy EAV design or adding/altering columns as metadata evolves, contradicting the need for agile indexing of heterogeneous attributes. Therefore this combination burdens both file delivery and metadata flexibility.
When this WOULD be correct
A company needs to store and stream video files to a small number of users within a corporate network, and the metadata requires complex relational queries (e.g., joining with user permissions). In that case, Azure Files for shared access and Azure SQL Database for relational metadata would be appropriate.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Blob Storage for videos and Azure Cosmos DB for metadataCorrect answer▾
Why this is correct
Azure Blob Storage is purpose-built for large unstructured binary data: it offers high-throughput write/read, configurable access tiers, and HTTPS-based access suitable for storing and delivering video files at scale. Azure Cosmos DB complements this by storing video metadata as flexible JSON documents, with automatic indexing and sub-millisecond point reads that support rich queries on tags, durations, and upload dates. This pairing keeps the media payload and its searchable catalog decoupled, so storage optimization and query performance are each handled by the most appropriate service.
✗Azure Blob Storage for both videos and metadataWrong answer — click to see why▾
Why this is wrong here
Azure Blob Storage is optimized for unstructured data like video files, but it lacks native querying capabilities for JSON metadata. Storing metadata in Blob Storage would require scanning all blobs or using external indexing, failing to meet the low-latency query requirement by tag.
★ When this WOULD be the correct answer
A company stores large video files and associated metadata as JSON documents, but the application only needs to stream videos and does not require any querying of metadata. In that case, Azure Blob Storage for both videos and metadata would be sufficient.
Why candidates choose this
Candidates may think that since both videos and metadata are stored as files (JSON), Blob Storage can handle both, overlooking the need for fast, indexed queries on metadata that Blob Storage does not natively support.
✗Azure Cosmos DB for videos and Azure Table Storage for metadataWrong answer — click to see why▾
Why this is wrong here
Azure Cosmos DB is not optimized for storing large video files; it is a NoSQL database designed for low-latency queries on structured data. Using it for videos would be cost-inefficient and would not support streaming workloads as effectively as Blob Storage.
★ When this WOULD be the correct answer
If the question asked for storing small media files (e.g., thumbnails) with metadata that requires global distribution and low-latency queries, and the videos were stored elsewhere, then Cosmos DB for media and Table Storage for metadata could be correct.
Why candidates choose this
Candidates may think Cosmos DB's multi-model capabilities can handle any data type, including videos, and that Table Storage is a natural fit for metadata, overlooking the specialized streaming and cost benefits of Blob Storage for large files.
✗Azure Files for videos and Azure SQL Database for metadataWrong answer — click to see why▾
Why this is wrong here
Azure Files is designed for file shares accessed via SMB protocol, not optimized for low-latency video streaming to users worldwide. Azure SQL Database is a relational database, which is not ideal for quickly querying JSON metadata by tag compared to a NoSQL solution like Cosmos DB.
★ When this WOULD be the correct answer
A company needs to store and stream video files to a small number of users within a corporate network, and the metadata requires complex relational queries (e.g., joining with user permissions). In that case, Azure Files for shared access and Azure SQL Database for relational metadata would be appropriate.
Why candidates choose this
Candidates may think Azure Files is suitable for video storage because it supports file sharing, and Azure SQL Database is a familiar choice for storing structured data, overlooking the specific requirements for low-latency global streaming and flexible JSON querying.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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 |
Go deeper
Related to this question
Learn chapter
Azure SQL Services
Key term
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
Key term
Azure Cosmos DB
Azure Cosmos DB is a fully managed, globally distributed NoSQL database service that offers fast reads and writes anywhere in the world with automatic scaling and multiple consistency models.
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Senior Network & Security Engineer · founder of Courseiva
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.