DP-900 Describe core data concepts Practice Question
A social media application allows users to post updates and like posts. After a user clicks the like button, the like count must update immediately and be exactly the same for all users viewing the post. Which data consistency model best fits this requirement?
⚠ Common exam trap
Microsoft often tests the misconception that 'eventual consistency' is acceptable for real-time updates, but the key differentiator here is the requirement for immediate and identical visibility for all users, which only strong consistency satisfies.
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 after a write operation (like clicking the like button) completes, any subsequent read operation returns the most recent write. This guarantees that all users viewing the post see the exact same, up-to-date like count immediately. This is required for the social media scenario where the like count must be identical for all viewers without any delay.
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 a replication model where replicas asynchronously converge, so reads may return stale data until all replicas are updated. During that window, the like count could display an incorrect value that changes across refreshes, or a user's like might be missing for others. For a feature that demands immediate, accurate feedback like a like count, this model is unsuitable because it tolerates those temporary inconsistencies.
- ✓
Strong consistency
Why this is correct
Strong consistency guarantees that a read always returns the most recently committed write, regardless of which replica receives the request. This is achieved via synchronous replication or quorum-based protocols that ensure no read is served before all relevant replicas agree on the latest state. For a like count, this means every user sees the same, up-to-date total immediately after a like is recorded, which is why it is the correct answer.
- ✗
Session consistency
Why it's wrong here
Session consistency, also known as read-your-writes consistency, ensures that within the same session a user's own writes are immediately visible to that user, but writes from other sessions may not be visible until later. Applied to like counts, a user would see their own like reflected instantly, but other users might not see that like, and the user might not see likes made by others until a later read. This yields different views of the like count across users, failing the requirement for globally accurate instantaneous totals.
- ✗
Bounded staleness consistency
Why it's wrong here
Bounded staleness consistency allows reads to be stale by a defined limit, such as at most 'K' versions or 'T' seconds behind the latest write. For example, a read could return the like count as it was up to five seconds ago, which would be technically 'consistent' within that bound. Because the product requires the like count to be accurate at the moment it is displayed, even a small bounded lag would show an outdated total to some users, so this model does not meet the requirement.
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