Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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?
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
Best answer
Replace the clustered columnstore index with a clustered rowstore index and add a nonclustered columnstore index
Correct. The rowstore index accelerates inserts, and the nonclustered columnstore index enables fast analytical queries.
B
Distractor review
Use memory-optimized tables for the entire table
Incorrect. Memory-optimized tables optimize OLTP but do not support columnstore indexes, hurting analytical query performance.
C
Distractor review
Partition the table by date and move older partitions to columnstore
Incorrect. This still requires a columnstore on most data and does not solve the insert performance issue on the active partition.
D
Distractor review
Keep the clustered columnstore index and use batch inserts
Incorrect. Batch inserts help but columnstore indexes still have overhead for small, frequent operations; the degradation remains.
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: Replace the clustered columnstore index with a clustered rowstore index and add a nonclustered columnstore index — A clustered columnstore index is optimized for analytics but degrades insert performance. The best solution is to use a clustered rowstore index (typically on the primary key) for fast single-row inserts and then add a nonclustered columnstore index on the table. The columnstore index will be updated in the background and still provide good query performance for analytical scans. Memory-optimized tables are for OLTP but would require schema changes and may not support columnstore. Partitioning does not directly solve the insert vs. columnstore conflict.
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.
Discussion
Loading comments…
Sign in to join the discussion.