- A
Strong consistency
Why wrong: Strong consistency ensures that any read always returns the most recent write, regardless of region. This would make the like count immediately visible to all users, but it reduces availability and increases latency, which the platform explicitly wants to avoid.
- B
Bounded staleness consistency
Why wrong: Bounded staleness allows reads to lag behind writes by a defined time interval or number of versions. This still imposes a bound on inconsistency, whereas the scenario describes eventual consistency with no strict bound.
- C
Session consistency
Why wrong: Session consistency guarantees monotonic reads, monotonic writes, read-your-writes, and write-follows-reads within a single client session. This does not extend to global eventual consistency across all users and regions.
- D
Eventual consistency
Eventual consistency is the weakest consistency level, prioritizing availability and low latency. It guarantees that if no new writes are made, all replicas will converge to the same state over time. This aligns with the platform's design goals.
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
This DP-900 practice question tests your understanding of describe considerations for working with non-relational data on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A global social media platform allows users to like posts. The platform is designed to prioritize availability and partition tolerance over strong consistency across its globally distributed Azure Cosmos DB instance. When a user likes a post, the like count may not be immediately visible to all users, but it will eventually become consistent across all regions. Which consistency model does this application follow?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"immediately / without restart"Why it matters: Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
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
Eventual consistency
Eventual consistency is the correct choice because the platform prioritizes availability and partition tolerance (AP from the CAP theorem) over strong consistency. In Azure Cosmos DB, eventual consistency guarantees that all replicas will converge to the same value over time without any ordering guarantees, which matches the scenario where like counts are not immediately visible but become consistent eventually.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Strong consistency
Why it's wrong here
Strong consistency ensures that any read always returns the most recent write, regardless of region. This would make the like count immediately visible to all users, but it reduces availability and increases latency, which the platform explicitly wants to avoid.
- ✗
Bounded staleness consistency
Why it's wrong here
Bounded staleness allows reads to lag behind writes by a defined time interval or number of versions. This still imposes a bound on inconsistency, whereas the scenario describes eventual consistency with no strict bound.
- ✗
Session consistency
Why it's wrong here
Session consistency guarantees monotonic reads, monotonic writes, read-your-writes, and write-follows-reads within a single client session. This does not extend to global eventual consistency across all users and regions.
- ✓
Eventual consistency
Why this is correct
Eventual consistency is the weakest consistency level, prioritizing availability and low latency. It guarantees that if no new writes are made, all replicas will converge to the same state over time. This aligns with the platform's design goals.
Clue confirmation
The clue word "immediately / without restart" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'eventual consistency' with 'session consistency' because both involve delays, but session consistency is scoped to a single client session and provides stronger guarantees like monotonic reads, whereas eventual consistency has no such session-level guarantees and is the weakest model in Cosmos DB.
Trap categories for this question
Scenario analysis trap
Bounded staleness allows reads to lag behind writes by a defined time interval or number of versions. This still imposes a bound on inconsistency, whereas the scenario describes eventual consistency with no strict bound.
Detailed technical explanation
How to think about this question
Under the hood, Azure Cosmos DB implements eventual consistency using a multi-master replication model with asynchronous propagation; writes are accepted in any region and replicated via a gossip protocol. This means that in the absence of conflicts, replicas will eventually converge, but during network partitions, stale reads are possible. A real-world scenario is a social media 'like' counter that shows different counts to users in different regions for a short period, which is acceptable for non-critical metrics.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe considerations for working with non-relational data on Azure — This question tests Describe considerations for working with non-relational data on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Eventual consistency — Eventual consistency is the correct choice because the platform prioritizes availability and partition tolerance (AP from the CAP theorem) over strong consistency. In Azure Cosmos DB, eventual consistency guarantees that all replicas will converge to the same value over time without any ordering guarantees, which matches the scenario where like counts are not immediately visible but become consistent eventually.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "immediately / without restart". Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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