Question 53 of 982

Identify considerations for relational data on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to create a columnstore index on the table. This is correct because a columnstore index stores data column-wise rather than row-wise, enabling batch processing that dramatically accelerates aggregation queries like SUM, COUNT, and GROUP BY. For a 100-million-row sales table, this approach reduces I/O and CPU by reading only the columns needed for the aggregation, such as product category and sales amount, instead of scanning every row. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of when to choose columnstore over traditional rowstore indexes or other structures like hash indexes. A common trap is assuming a clustered index on the date column would help, but for heavy aggregations on large tables, columnstore is the proven solution. Memory tip: think “columns for counts”—when you need to crunch numbers across millions of rows, store data by column to speed up the sum.

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 business analyst needs to query a large Azure SQL Database table that stores sales transactions. The table contains over 100 million rows. The analyst wants to retrieve aggregated sales per product category for the current month. The current query performs a full table scan and takes several minutes. Which indexing strategy will best improve the performance of this aggregation query?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

Question 1mediummultiple choice

Full question →

A
Create a clustered index on the transaction date column
Why wrong: A clustered index on date helps with range scans by date but does not efficiently support summing values by category across many rows.
B
Create a nonclustered index on the product category column
Why wrong: A nonclustered index on category helps when filtering by a specific category, but the aggregation needs to scan all categories for the month, still requiring a full scan of the index.
C
Create a columnstore index on the table
A columnstore index is purpose-built for analytical queries that aggregate over large tables, using column-wise storage and advanced compression to reduce I/O.
D
Create a filtered index on transactions from the current month
Why wrong: A filtered index on a volatile date range would need to be rebuilt frequently and does not efficiently support aggregation by category across all rows.

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

✓

Create a columnstore index on the table

A columnstore index stores data column-wise and uses batch processing, which dramatically accelerates aggregation queries (like SUM, COUNT, GROUP BY) over large tables. For a 100-million-row table, this reduces I/O and CPU by reading only the columns needed for the aggregation, making it the optimal choice for the analyst's current-month sales-per-category query.

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.

✗
Create a clustered index on the transaction date column
Why it's wrong here
A clustered index on date helps with range scans by date but does not efficiently support summing values by category across many rows.
✗
Create a nonclustered index on the product category column
Why it's wrong here
A nonclustered index on category helps when filtering by a specific category, but the aggregation needs to scan all categories for the month, still requiring a full scan of the index.
✓
Create a columnstore index on the table
Why this is correct
A columnstore index is purpose-built for analytical queries that aggregate over large tables, using column-wise storage and advanced compression to reduce I/O.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a filtered index on transactions from the current month
Why it's wrong here
A filtered index on a volatile date range would need to be rebuilt frequently and does not efficiently support aggregation by category across all rows.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose a filtered or nonclustered index thinking they will reduce the scan scope, but they overlook that columnstore indexes are specifically designed for high-performance analytical aggregations on large tables, not just for filtering or single-column lookups.

Detailed technical explanation

How to think about this question

Columnstore indexes use columnar compression (e.g., dictionary encoding, run-length encoding) and batch mode execution, which can process up to 900 rows per batch instead of row-by-row. In Azure SQL Database, a clustered columnstore index is the primary storage for large fact tables in data warehousing scenarios, and it can reduce query times from minutes to seconds for aggregations over hundreds of millions of rows.

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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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?

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: Create a columnstore index on the table — A columnstore index stores data column-wise and uses batch processing, which dramatically accelerates aggregation queries (like SUM, COUNT, GROUP BY) over large tables. For a 100-million-row table, this reduces I/O and CPU by reading only the columns needed for the aggregation, making it the optimal choice for the analyst's current-month sales-per-category query.

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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

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 retail company runs analytical reporting queries on a large Sales table in Azure SQL Database. The table contains over 100 million rows and is updated daily with new transactions. The queries aggregate data by product and month, scanning millions of rows per query. The company wants to significantly reduce query execution time without changing the queries. Which indexing strategy should they implement?

medium

✓ A.Create a clustered columnstore index on the table.
B.Create a nonclustered index on the ProductID column.
C.Create a filtered index for the most recent month's data.
D.Create a clustered rowstore index (default) and rely on database compression.

Why A: A clustered columnstore index is ideal for large data warehousing and analytical workloads because it stores data column-wise, enabling high compression and batch-mode processing. For queries that aggregate millions of rows by product and month, columnstore indexes dramatically reduce I/O and CPU by scanning only the necessary columns and using segment elimination, which directly addresses the requirement to reduce query execution time without changing the queries.

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