DP-900 Describe core data concepts Practice Question
A media company stores metadata about its video assets. Each asset has a fixed set of fields: AssetID, Title, DurationSeconds, and UploadDate. Analysts run daily reports that sum DurationSeconds grouped by UploadDate. The company wants the storage engine to enforce that DurationSeconds is numeric and that each AssetID is unique. Which characteristic of the data most directly justifies choosing a relational database over a non-relational store?
⚠ Common exam trap
The trap here is picking a justification based on reporting or volume, when the scenario's real driver is enforcement of data types and uniqueness across a uniform set of fields.
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
✓
The metadata fields are identical for every asset and require type and uniqueness enforcement
The uniform field set combined with explicit requirements for numeric typing and a unique AssetID points to a relational model, where such constraints are declared in the schema and enforced by the engine. Non-relational stores typically leave typing and uniqueness to application code, which weakens guarantees. Aggregation and client type do not distinguish the models here, so the structural enforcement requirement is the decisive factor.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The metadata will be consumed by a web application rather than a desktop client
Why it's wrong here
The consuming client type does not determine the underlying data model. A web application can read from relational or non-relational stores equally well through appropriate drivers and APIs. The decisive factor in this scenario is the need for enforced types and uniqueness across a uniform set of fields, not the presentation layer that reads the data. Client technology is orthogonal to storage model selection.
- ✗
The reports aggregate numeric values, which only relational engines can compute
Why it's wrong here
Aggregation of numeric values is not exclusive to relational engines; document stores, columnar engines, and others support summation and grouping. The real differentiator in this scenario is enforcement of a numeric type for DurationSeconds and uniqueness of AssetID. Claiming aggregation is relational-only is technically incorrect and would lead to a flawed justification even though the final choice may be right.
- ✗
The data volume is expected to grow quickly over time
Why it's wrong here
High data volume alone does not dictate relational versus non-relational; both scale to large datasets with different tradeoffs. The scenario emphasizes strict types and uniqueness rather than scale. Choosing a relational database purely because volume grows would ignore the actual requirement, which is enforcement of numeric typing and a unique identifier. Volume is a capacity concern, not a structural one.
- ✓
The metadata fields are identical for every asset and require type and uniqueness enforcement
Why this is correct
Every asset shares the same fields, and the company explicitly wants DurationSeconds constrained to numeric and AssetID to be unique. A relational database enforces column data types and unique or primary key constraints declaratively. This uniform, well-defined structure is exactly the situation where a relational schema provides value, rather than the flexible schemas used by non-relational stores.
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Related to this question
Learn chapter
Azure Data Lake Storage Gen2 Hierarchical Namespace
Key term
Relational database
A relational database organizes data into tables with rows and columns, where each table relates to others using unique keys, allowing efficient storage, retrieval, and manipulation of structured information.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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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