DP-900 Describe core data concepts Practice Question
Your organization uses Azure Cosmos DB for a real-time inventory application. The data includes a container with items that have a `category` property. The operations team frequently queries for all items in a specific category. To optimize query performance and minimize request unit (RU) consumption, you decide to implement a materialized view. Which Azure Cosmos DB feature should you use to achieve this?
⚠ Common exam trap
A common mix-up: candidates confuse the change feed (a reactive stream of changes) with materialized views (a persisted, queryable snapshot), or assume that composite indexes alone can achieve the same pre-computation benefits as a materialized view.
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
✓
Materialized views (preview)
Azure Cosmos DB's materialized views (preview) feature allows you to pre-join, aggregate, and transform data from a source container into a separate container optimized for specific query patterns, such as filtering by `category`. This reduces RU consumption by avoiding full scans or expensive cross-partition queries, as the view is pre-computed and indexed according to the target query.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition key design
Why it's wrong here
Partition key design is about choosing a property that evenly distributes data and request traffic across physical partitions in Azure Cosmos DB. While a well-chosen partition key improves scalability and query routing efficiency, it does not generate or store pre-computed query results. It is a data modeling decision, not a mechanism for creating materialized views, so it cannot satisfy the requirement to optimize common queries with automatically updated, stored results.
- ✗
Change feed
Why it's wrong here
The change feed in Azure Cosmos DB is an append-only log of item-level changes (inserts, updates, deletes) that can be consumed by triggers, functions, or external processors. It provides a streaming mechanism to detect and react to data modifications, but it does not itself define or maintain a denormalized, pre-computed view. Although you could use the change feed to build a materialized view in another system, the change feed is not the feature that creates or stores materialized views within Cosmos DB.
- ✗
Composite indexes
Why it's wrong here
Composite indexes in Azure Cosmos DB improve query performance by enabling efficient multi-field lookups and sorting without scanning the entire container. However, an index is a data structure that accelerates query execution; it does not contain the results of a query or pre-compute aggregated/denormalized data. Composite indexes do not create a materialized view, which requires storing the output of a query and keeping it synchronized with the source container.
- ✓
Materialized views (preview)
Why this is correct
Materialized views (preview) in Azure Cosmos DB are the correct feature because they let you define a view as a query over a source container, and the service automatically maintains a separate, denormalized container with the query results. This view is updated incrementally when source data changes and is optimized for read-heavy or common-query scenarios, reducing latency and cost. By pre-computing and storing the results, materialized views directly fulfill the requirement for a built-in, automatically refreshed mechanism.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Request unit
A request unit (RU) is a normalized measure of the computational capacity and resources needed to perform a database operation in Azure Cosmos DB.
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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