DP-900 Describe core data concepts Practice Question
A company stores customer orders. Each order has a unique order ID, customer ID, a list of items (each item contains product ID, quantity, and price), and an order date. They frequently query orders by customer ID and also need to filter by order date ranges. The data volume is high and schema flexibility is desired because items can vary in structure. Which type of data store is best suited for this scenario?
⚠ Common exam trap
Many candidates choose a relational database (Option A) because they think 'orders' and 'items' imply a need for joins, but the requirement for schema flexibility and high-volume queries by customer ID and date range actually points to a document store, which can embed items directly and index the relevant 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
✓
Document database
A document database (e.g., Azure Cosmos DB for NoSQL) is ideal because it stores each order as a self-contained JSON document, allowing the items array to vary in structure per order (schema flexibility). It supports efficient queries by customer ID (using a partition key) and filtering by order date ranges (using indexing on the date field), while handling high data volumes with horizontal scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Relational database
Why it's wrong here
A relational database could store this order data, but it would require decomposing the order into multiple normalized tables—Customers, Orders, and OrderItems—linked by foreign keys. Queries to retrieve a full order with its items demand complex JOIN operations and a fixed schema enforced upfront. This makes it more cumbersome than a document database for this embedded, nested order structure, even though it's technically possible.
- ✗
Key-value store
Why it's wrong here
A key-value store, such as Azure Table Storage or Redis, is optimized for point lookups using a single primary key, like an order ID. It does not natively support filtering on multiple attributes such as customer ID or order date; you would need to scan all items or maintain separate secondary index tables manually. This makes it a poor fit for querying orders by customer or date, which the scenario implies.
- ✓
Document database
Why this is correct
A document database like Azure Cosmos DB stores an entire order as a single JSON document, with the order's line items embedded as an array within that document. This matches the natural order-with-items hierarchy and allows atomic reads and writes for the whole order. It also supports scalable secondary indexing and a rich SQL-like query language to filter on customer ID, order date, or item details without joins.
- ✗
Graph database
Why it's wrong here
A graph database, such as Azure Cosmos DB Gremlin or Azure Neptune, models data as nodes and edges and excels at traversing many-to-many relationships. Orders, customers, and items are not inherently relationship-centric—they form a simple hierarchy that is better served by a document structure. Using a graph database would require modeling every order as a node with edges to items and customers, which adds unnecessary complexity and performance overhead for simple range or equality queries.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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.
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
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