- A
A: In-memory OLTP
Why wrong: In-memory OLTP optimizes memory-optimized tables for high-speed transactions but does not provide columnstore-based analytics on the same table in a complementary manner.
- B
B: Elastic Query
Why wrong: Elastic Query enables querying across multiple Azure SQL databases; it does not address performance trade-offs within a single table.
- C
C: Hyperscale service tier
Why wrong: Hyperscale is a service tier that provides fast scaling and large storage, but it does not inherently solve the conflict between columnstore inserts and rowstore inserts.
- D
D: Convert the table to a rowstore heap with a nonclustered columnstore index
A nonclustered columnstore index on a rowstore table allows efficient OLTP inserts into the rowstore while the columnstore index periodically processes batches for analytical performance, achieving a balanced hybrid workload.
Quick Answer
The correct answer is to convert the table to a rowstore heap with a nonclustered columnstore index. This hybrid indexing approach separates the OLTP insert path from the OLAP read path, allowing the rowstore heap to handle high-volume singleton inserts efficiently while the nonclustered columnstore index still enables fast analytical queries on large datasets. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of workload-optimized storage structures, specifically how to balance real-time ingestion against analytical performance in a hybrid workload. A common trap is assuming a clustered columnstore index is always best for analytics, but it degrades insert performance due to delta store overhead. Remember the memory tip: "Heap for speed, columnstore for reads"—the heap absorbs inserts like a sponge, while the columnstore snapshot provides the analytical view.
DP-900 Practice Question: Identify considerations for relational data on Azure
This DP-900 practice question tests your understanding of identify considerations for 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 company runs a financial application on Azure SQL Database. The Transactions table has a clustered columnstore index to support fast analytical queries on large historical datasets. However, the application also ingests a high volume of new transactions each second, and the columnstore index is causing performance degradation for these real-time inserts. The workload is hybrid (OLTP and OLAP). Which feature should the company implement to improve insert performance while still enabling efficient analytical queries on the table?
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
D: Convert the table to a rowstore heap with a nonclustered columnstore index
Option D is correct because converting the table to a rowstore heap with a nonclustered columnstore index allows the table to handle high-volume singleton inserts efficiently (rowstore heap) while still enabling fast analytical queries via the nonclustered columnstore index. This hybrid approach separates the OLTP insert path from the OLAP read path, avoiding the overhead of maintaining a clustered columnstore index during real-time ingestion.
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.
- ✗
A: In-memory OLTP
Why it's wrong here
In-memory OLTP optimizes memory-optimized tables for high-speed transactions but does not provide columnstore-based analytics on the same table in a complementary manner.
- ✗
B: Elastic Query
Why it's wrong here
Elastic Query enables querying across multiple Azure SQL databases; it does not address performance trade-offs within a single table.
- ✗
C: Hyperscale service tier
Why it's wrong here
Hyperscale is a service tier that provides fast scaling and large storage, but it does not inherently solve the conflict between columnstore inserts and rowstore inserts.
- ✓
D: Convert the table to a rowstore heap with a nonclustered columnstore index
Why this is correct
A nonclustered columnstore index on a rowstore table allows efficient OLTP inserts into the rowstore while the columnstore index periodically processes batches for analytical performance, achieving a balanced hybrid workload.
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 assume a clustered columnstore index is always the best choice for analytical queries, overlooking the significant insert performance penalty it imposes on high-volume OLTP workloads, and fail to recognize that a nonclustered columnstore index on a heap can provide the same analytical benefits without the insert bottleneck.
Detailed technical explanation
How to think about this question
A clustered columnstore index stores all data in columnar format, which is optimized for read-heavy analytical workloads but suffers from high overhead during singleton inserts because each row must be written to a delta rowgroup before being compressed into the main columnstore. By using a rowstore heap (no clustered index) with a nonclustered columnstore index, inserts go directly into the heap with minimal overhead, and the nonclustered columnstore index asynchronously batches and compresses data for analytical queries, balancing OLTP and OLAP performance.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this DP-900 question test?
Identify considerations for relational data on Azure — This question tests Identify considerations for 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: D: Convert the table to a rowstore heap with a nonclustered columnstore index — Option D is correct because converting the table to a rowstore heap with a nonclustered columnstore index allows the table to handle high-volume singleton inserts efficiently (rowstore heap) while still enabling fast analytical queries via the nonclustered columnstore index. This hybrid approach separates the OLTP insert path from the OLAP read path, avoiding the overhead of maintaining a clustered columnstore index during real-time ingestion.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Same concept, more angles
1 more ways this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial application uses Azure SQL Database. The workload consists of a high volume of small, frequent insert operations (OLTP) and periodic complex analytical queries that scan large portions of the same table (OLAP). The table currently has a clustered columnstore index. The inserts are suffering from performance degradation. What should the company do to improve insert performance while still enabling efficient analytical queries?
hard- ✓ A.Replace the clustered columnstore index with a clustered rowstore index and add a nonclustered columnstore index
- B.Use memory-optimized tables for the entire table
- C.Partition the table by date and move older partitions to columnstore
- D.Keep the clustered columnstore index and use batch inserts
Why A: Option A is correct because a clustered rowstore index is optimized for high-volume OLTP inserts, while adding a nonclustered columnstore index allows the same table to support efficient analytical queries by providing a separate columnar structure. This hybrid approach avoids the insert overhead of columnstore indexes, which are designed for bulk operations and can suffer from small, frequent insert performance degradation due to delta store management and tuple mover processes.
Last reviewed: Jun 11, 2026
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