hardmultiple choiceObjective-mapped

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?

Question 1hardmultiple choice
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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

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

A: In-memory OLTP

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

Distractor review

B: Elastic Query

Elastic Query enables querying across multiple Azure SQL databases; it does not address performance trade-offs within a single table.

C

Distractor review

C: Hyperscale service tier

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

Best answer

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.

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.

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Question 2

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Question 3

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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: D: Convert the table to a rowstore heap with a nonclustered columnstore index — A nonclustered columnstore index (NCCI) on a rowstore table (heap or clustered index) allows the table to maintain a traditional rowstore structure for efficient OLTP operations, while the NCCI periodically compresses data for analytical queries. This approach balances both workloads. In-memory OLTP (A) is for high-performance OLTP but does not directly help analytical queries on the same table and cannot be combined with columnstore easily. Elastic Query (B) is for querying remote databases. Hyperscale (C) is a service tier that scales storage and compute but does not specifically address the insert/analytics conflict. Therefore, option D is correct.

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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