Question 243 of 982

Describe core data concepts →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is eventual consistency. This model is correct because the application prioritizes availability and partition tolerance, meaning it accepts that data may be temporarily inconsistent across regions to ensure the system remains responsive and operational during network partitions. In an eventual consistency model, updates like a like count are propagated asynchronously, so reads may return stale data, but all replicas will converge to the same value over time. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of the CAP theorem trade-offs—specifically that choosing AP (availability and partition tolerance) forces a move away from strong consistency toward eventual consistency. A common trap is confusing eventual consistency with strong consistency or assuming it means data is never consistent. Remember the memory tip: “Eventually, everyone sees the same score—just not at the same time.”

DP-900 Describe core data concepts Practice Question

This DP-900 practice question tests your understanding of describe core data concepts. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 application allows users to post updates and 'like' posts. The application is designed to prioritize availability and partition tolerance over strong consistency. As a result, 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.

Question 1hardmultiple choice

Full question →

A
Strong consistency
Why wrong: Strong consistency ensures that any read operation returns the most recent write, but this comes at the cost of reduced availability and higher latency in distributed systems. The application explicitly prioritizes availability over strong consistency, so this is not the model used.
B
Eventual consistency
Eventual consistency guarantees that if no new updates are made, all replicas will eventually return the same value. This matches the scenario where updates are not immediately visible but become consistent over time, supporting high availability and partition tolerance.
C
Consistent prefix
Why wrong: Consistent prefix guarantees that reads always see a consistent prefix of the write history, meaning they never see partial writes out of order. While it provides some consistency, it does not guarantee eventual convergence of all writes in the way described.
D
Bounded staleness
Why wrong: Bounded staleness guarantees that reads will see writes within a specified time bound (e.g., 5 seconds). The scenario does not mention any time bound; it only says updates become consistent 'eventually', which aligns with eventual consistency rather than a bounded delay.

Full breakdown with real-world context →

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

The application prioritizes availability and partition tolerance, which aligns with the eventual consistency model. In this model, updates (like a 'like' count) are propagated asynchronously across replicas, and while reads may return stale data temporarily, all replicas will converge to the same value over time. This is typical of NoSQL systems like Apache Cassandra or Amazon DynamoDB when configured with eventual consistency.

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 operation returns the most recent write, but this comes at the cost of reduced availability and higher latency in distributed systems. The application explicitly prioritizes availability over strong consistency, so this is not the model used.
✓
Eventual consistency
Why this is correct
Eventual consistency guarantees that if no new updates are made, all replicas will eventually return the same value. This matches the scenario where updates are not immediately visible but become consistent over time, supporting high availability and partition tolerance.
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.
✗
Consistent prefix
Why it's wrong here
Consistent prefix guarantees that reads always see a consistent prefix of the write history, meaning they never see partial writes out of order. While it provides some consistency, it does not guarantee eventual convergence of all writes in the way described.
✗
Bounded staleness
Why it's wrong here
Bounded staleness guarantees that reads will see writes within a specified time bound (e.g., 5 seconds). The scenario does not mention any time bound; it only says updates become consistent 'eventually', which aligns with eventual consistency rather than a bounded delay.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse 'eventual consistency' with 'bounded staleness' because both allow stale reads, but eventual consistency has no guaranteed time or version bound, whereas bounded staleness imposes a strict limit—a distinction Microsoft explicitly tests in DP-900.

Trap categories for this question

Scenario analysis trap
Bounded staleness guarantees that reads will see writes within a specified time bound (e.g., 5 seconds). The scenario does not mention any time bound; it only says updates become consistent 'eventually', which aligns with eventual consistency rather than a bounded delay.

Detailed technical explanation

How to think about this question

Eventual consistency is a weak consistency model often implemented via gossip protocols (e.g., in Cassandra) or last-writer-wins conflict resolution. Under the hood, writes are accepted by any replica and propagated asynchronously; read requests may contact any replica, potentially returning a stale value until all replicas have received the update. This model is ideal for globally distributed applications where low-latency writes and high availability are critical, such as social media feeds or like counters, but it requires application-level handling of conflicts if needed.

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-900 question test?

Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Eventual consistency — The application prioritizes availability and partition tolerance, which aligns with the eventual consistency model. In this model, updates (like a 'like' count) are propagated asynchronously across replicas, and while reads may return stale data temporarily, all replicas will converge to the same value over time. This is typical of NoSQL systems like Apache Cassandra or Amazon DynamoDB when configured with eventual consistency.

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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