Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Strong consistency
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
Distractor review
Bounded staleness consistency
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
Distractor review
Session consistency
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
Best answer
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.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
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 guarantees that if no new updates are made, eventually all reads will see the latest write. This model sacrifices strong consistency for high availability and low latency, fitting the scenario where immediate global consistency is not required. Strong consistency ensures immediate visibility but reduces availability. Bounded staleness allows a defined lag, and session consistency is scoped to a user session.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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