DP-900 Describe core data concepts Practice Question
A company uses Azure Cosmos DB for a globally distributed application. They need to ensure low-latency reads and writes for users in multiple regions. Which consistency level provides the strongest guarantees without sacrificing availability?
⚠ Common exam trap
Watch out — candidates often confuse 'strongest guarantees' with 'strong consistency,' not realizing that strong consistency sacrifices availability during a partition, whereas bounded staleness is the strongest level that still guarantees high availability in a globally distributed setup.
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
✓
Bounded staleness
Bounded staleness provides the strongest consistency guarantee that still maintains availability during a partition. It ensures that reads are guaranteed to be within a configurable staleness window (either K versions or a time interval) from the latest write, which is stronger than consistent prefix or eventual consistency, while avoiding the availability trade-offs of strong consistency in a globally distributed Azure Cosmos DB account.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Bounded staleness
Why this is correct
Bounded staleness offers strong consistency with a configurable lag: it guarantees that reads within a specified time or operation count will not miss any committed write, after which they reflect the latest data. This is achieved without requiring synchronous cross-region coordination, preserving write availability and low latency. Because of this, it is the strongest consistency level that still supports globally distributed writes and reads with predictable freshness.
- ✗
Consistent prefix
Why it's wrong here
Consistent prefix ensures that reads never see out-of-order writes, so if writes A then B occur, a read will see A before B or both, but never B before A. However, it places no bound on how far behind a read can be, meaning a client might see very stale data with no recency guarantee. That makes it inappropriate when applications need any predictable freshness, unlike bounded staleness which provides a lag bound.
- ✗
Strong
Why it's wrong here
Strong consistency requires all writes to be acknowledged by every replica across all regions before returning success, ensuring reads always see the latest committed write. This synchronous synchronization adds significant latency and reduces write availability, as any temporary network partition or regional outage can block writes entirely. Therefore, while it offers the highest consistency, it directly conflicts with the need for high write availability in a globally distributed application.
- ✗
Eventual
Why it's wrong here
Eventual consistency provides no guarantees on recency: if no new writes occur, replicas will eventually converge, but until then reads can return outdated or even conflicting values. There is no ordering guarantee, so a read might see a write that happened later while missing an earlier one. This level is suitable for scenarios where freshness is not critical, but not for an app that needs any defined consistency window for globally distributed operations.
Go deeper
Related to this question
Learn chapter
Azure SQL Services
Key term
Consistency level
Consistency level is a setting in Azure data services that determines how quickly and accurately data is synchronized across multiple copies in a distributed system.
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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