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 ride-sharing application uses Azure Cosmos DB for trip data. Each trip record contains TripID (unique), DriverID, RiderID, TripDate, and other details. The most common query retrieves all trips for a specific driver within a given date range. Which partition key should be chosen to minimize Request Unit (RU) consumption and ensure even data distribution?
⚠ Common exam trap
The trap here is that candidates often pick TripDate because it seems logical for date-range queries, but they overlook that the primary filter is DriverID, and partitioning by TripDate would cause cross-partition queries and potential hot spots on high-traffic dates.
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
✓
DriverID
DriverID is the optimal partition key because the most common query filters on DriverID and a date range. Partitioning by DriverID ensures that all trips for a specific driver are stored in the same physical partition, making the query a single-partition operation that consumes minimal Request Units (RUs). It also provides even data distribution across partitions because each driver generates a roughly similar number of trips, avoiding hot spots.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
TripID
Why it's wrong here
TripID is unique per trip, so using it as partition key would result in each trip being in its own partition. Queries filtering by DriverID would become cross-partition queries, consuming more RU and having higher latency.
- ✓
DriverID
Why this is correct
DriverID aligns with the most common query pattern. All trips for a given driver are stored together, allowing single-partition queries. This minimizes RU consumption if the number of trips per driver is within the 20 GB logical partition limit.
- ✗
TripDate
Why it's wrong here
Using TripDate as the partition key creates a time-series hot spot: all trips from the same day, especially peak days like holidays or weekends, land in one logical partition, causing throttled throughput and unbalanced request-unit consumption. Moreover, a driver who has trips across multiple days would require cross-partition queries for every driver-based lookup because no single partition contains that driver's complete history. Date-based keys also suffer from append-heavy access patterns, where the current day's partition becomes a bottleneck while older partitions sit idle.
- ✗
RiderID
Why it's wrong here
Although RiderID offers high cardinality and would evenly distribute write operations, it fails the primary access pattern: the application queries trips by driver to compute earnings or routes, not by rider. Because each rider's trips are stored in a different physical partition, a DriverID filter forces Cosmos DB to fan out across all partitions and merge results, escalating request-unit costs and adding noticeable latency. A partition key must align with the most frequent query predicate, and RiderID only supports rider-centric workloads, not the driver-focused trip reporting this system requires.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Partition key
A partition key is a value used by database systems to distribute data across multiple storage partitions, enabling faster queries and efficient scaling.
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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