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 global e-commerce platform uses Azure Cosmos DB to store product inventory data. Customers add items to their cart, which reduces the available inventory count. The application requires that after a customer adds an item, any subsequent read of that product's inventory from any region in the world must reflect the reduced count immediately. Which Cosmos DB consistency level should be used?
⚠ Common exam trap
Watch out — candidates often assume Session consistency is sufficient because it provides 'read your writes' within a session, but the question explicitly requires immediate global visibility for any subsequent read from any region, which only Strong consistency can guarantee.
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
✓
Strong consistency
Strong consistency ensures that any read operation returns the most recent write, regardless of the region. Since the application requires that after a customer adds an item, any subsequent read of that product's inventory from any region must reflect the reduced count immediately, Strong consistency is the only level that guarantees linearizability and zero staleness across all replicas.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Eventual consistency
Why it's wrong here
Eventual consistency is the weakest level in Azure Cosmos DB. It allows replicas to converge to the same state only when writes have stopped, and it does not guarantee an upper bound on staleness or any ordering of writes. As a result, a read can return arbitrarily outdated data, even from a region that just acknowledged a recent write, which fails the e-commerce platform's requirement that every read see the most recent committed write.
- ✗
Consistent prefix consistency
Why it's wrong here
Consistent prefix consistency preserves the order in which writes are observed by guaranteeing that a read never sees a write without first seeing all earlier writes in that prefix. However, it does not promise that the read reflects the latest write to an item; it may observe only an earlier state from the prefix while newer writes remain invisible. This means reads can still be stale, so it is insufficient for a scenario where inventory or order status must be globally up-to-date.
- ✗
Session consistency
Why it's wrong here
Session consistency confines its guarantees to a single client session using a session token. Within that session it provides monotonic reads, monotonic writes, and read-your-own-writes, but those guarantees do not extend to clients outside the session or to reads performed by the same client after a session ends. Therefore, a different session or a different region may not see the latest write, which fails the requirement that every read across the global commerce platform returns the most recent committed data.
- ✓
Strong consistency
Why this is correct
Strong consistency delivers linearizable reads, meaning every read returns the most recently committed write regardless of which replica or region serves the request. In Cosmos DB this is achieved by requiring writes to be acknowledged by a quorum of replicas before the write is confirmed, so no replica can serve a stale read afterward. For a global e-commerce platform, this prevents overselling and ensures order and inventory data are always current, though it increases write latency and can reduce availability under network partitions.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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