DP-900 Describe core data concepts Practice Question
A social media application displays the number of posts each user has created. After a user submits a new post, the count must reflect the update across all servers within a few seconds. Which data consistency model best describes this requirement?
⚠ Common exam trap
A common mix-up: candidates confuse 'eventual consistency' with 'weak consistency' or assume that any delay means strong consistency is required, but the key is the explicit tolerance of a few seconds, which aligns with eventual consistency's convergence 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
✓
Eventual consistency
Eventual consistency is correct because the requirement allows a few seconds for the update to propagate across all servers, meaning the system does not guarantee immediate uniformity but will converge to the same count eventually. This is typical in distributed systems like social media applications where high availability and partition tolerance are prioritized over immediate consistency, often using techniques like asynchronous replication.
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 (linearizability) would require every read to return the most recent write, meaning a post-count update would have to synchronously replicate to all nodes before the write is acknowledged. This dramatically increases write latency and reduces availability in a distributed system, especially with geographically distributed replicas. Since the product only requires the count to catch up within a few seconds after a post is created, strong consistency is overkill.
- ✓
Eventual consistency
Why this is correct
Eventual consistency allows updates to propagate asynchronously to replicas, guaranteeing that if no further updates occur, all replicas will return the same value after a short period. This matches the requirement of reflecting the update within a few seconds.
- ✗
Sequential consistency
Why it's wrong here
Sequential consistency imposes a single global order of all operations that respects the program order of each client, so an update to a post count and a subsequent read would be required to appear in that exact order across every replica. That is stricter than needed for a counter that only eventually converges, and it sacrifices performance to maintain a total order among all replicas. Eventual consistency makes no such ordering guarantee, so sequential consistency is an unnecessarily strict choice.
- ✗
Causal consistency
Why it's wrong here
Causal consistency only preserves the order of operations that are causally related, such as a user posting and then reading the updated count; unrelated increments to the same counter could be applied in different orders on different replicas. In a social-media counter, most post-count updates are independent and not causally related, so preserving causal order adds complexity without any functional benefit. Eventual consistency is sufficient because the final converged value is all that matters.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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