DP-900 Describe core data concepts Practice Question
A media company catalogues video interviews stored in Azure Blob Storage. Each file is an MP4 with no embedded metadata describing speaker, topic, or duration. Producers want to search the catalogue later by those attributes. What should the company do to make the videos searchable?
⚠ Common exam trap
The trap here is treating unstructured media as if it could be made searchable by changing its storage container, when the real requirement is extracting queryable metadata about the media.
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
✓
Extract descriptive metadata into a semi-structured or structured index alongside the videos.
The videos are unstructured content, so the searchable attributes must be captured separately as structured or semi-structured metadata that points back to each file. Converting video to relational rows, trusting file names, or querying binary columns does not produce queryable speaker, topic, and duration fields. A metadata index alongside Blob Storage is the workable design.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert each MP4 into a relational table with one row per video.
Why it's wrong here
A relational table stores structured rows and columns, not binary video content itself. You can store a pointer to the file and metadata columns, but conversion alone does not extract speaker, topic, or duration from the video. This option misstates what relational storage does and leaves the searchable attributes unpopulated, so producers still could not search by those fields.
- ✗
Load the MP4 files directly into a columnstore table and query the binary column.
Why it's wrong here
Columnstore indexes are designed for large volumes of structured analytical data and do not make raw binary video searchable by semantic attributes. Querying a binary column returns bytes, not speaker or topic values. This approach adds storage overhead without delivering the field-level search the producers need, and it misuses a feature intended for structured analytics.
- ✓
Extract descriptive metadata into a semi-structured or structured index alongside the videos.
Why this is correct
Because the videos themselves are unstructured binary content, the practical approach is to keep them in Blob Storage and extract searchable attributes such as speaker, topic, and duration into a companion index. That index can be a relational table or a document store, enabling efficient queries. This is the standard pattern for cataloguing unstructured assets while preserving the original files.
- ✗
Store the videos as-is and rely on file names to provide the search attributes.
Why it's wrong here
File names are unstructured strings and the scenario states the MP4s have no embedded metadata. Renaming files or relying on their names does not create queryable speaker, topic, or duration fields, and it scales poorly across a large catalogue. Search on unstructured names is unreliable, so this approach does not meet the producers' requirement to search by those attributes.
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
Non-Relational DB Types: Document, Key-Value, Graph, Column
Key term
Index
An index is a data structure that speeds up data retrieval operations on a database table or file, much like a book index helps you find topics quickly.
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.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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