Question 304 of 982

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

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 uses Azure SQL Database for its order management system. The database has a table named Orders with columns OrderID (INT, PRIMARY KEY), CustomerID (INT), OrderDate (DATE), TotalAmount (DECIMAL). Queries that filter by OrderDate are slow. The database administrator observes that the nonclustered index on OrderDate has high fragmentation and many page splits. Which action will most likely improve query performance for these date-based queries?

Clue words in this question

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

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

Question 1mediummultiple choice

Full question →

A
Rebuild the nonclustered index on OrderDate with a FILLFACTOR of 80.
Rebuilding with a lower fill factor reserves free space on pages, reducing page splits and fragmentation, thus improving performance.
B
Change the data type of TotalAmount from DECIMAL to FLOAT.
Why wrong: Changing the data type does not address fragmentation or page splits and may reduce precision.
C
Remove the clustered index on OrderID and create a clustered index on OrderDate.
Why wrong: A clustered index on OrderDate would physically order data by date, but frequent inserts on the same date could cause contention and hot spots, and rebuilding the table is disruptive.
D
Add a columnstore index on the OrderDate column.
Why wrong: Columnstore indexes are optimized for analytical queries on large tables, not for point lookups or range scans with frequent updates in OLTP workloads.

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

✓

Rebuild the nonclustered index on OrderDate with a FILLFACTOR of 80.

Rebuilding the nonclustered index on OrderDate with a FILLFACTOR of 80 reduces page splits by leaving free space in each leaf-level page. This accommodates future insertions and updates that modify the OrderDate values, lowering fragmentation and improving query performance for date-based filters.

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.

✓
Rebuild the nonclustered index on OrderDate with a FILLFACTOR of 80.
Why this is correct
Rebuilding with a lower fill factor reserves free space on pages, reducing page splits and fragmentation, thus improving performance.
Clue confirmation
The clue words "most likely", "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the data type of TotalAmount from DECIMAL to FLOAT.
Why it's wrong here
Changing the data type does not address fragmentation or page splits and may reduce precision.
✗
Remove the clustered index on OrderID and create a clustered index on OrderDate.
Why it's wrong here
A clustered index on OrderDate would physically order data by date, but frequent inserts on the same date could cause contention and hot spots, and rebuilding the table is disruptive.
✗
Add a columnstore index on the OrderDate column.
Why it's wrong here
Columnstore indexes are optimized for analytical queries on large tables, not for point lookups or range scans with frequent updates in OLTP workloads.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may think changing the clustered index to OrderDate (Option C) is the best solution, but they overlook that this would disrupt the primary key and cause even more fragmentation for a table with frequent inserts, whereas rebuilding with a lower fill factor directly addresses page splits without altering the table's physical design.

Detailed technical explanation

How to think about this question

Page splits occur when an index page fills and a new row must be inserted in key order, causing half the rows to move to a new page. A FILLFACTOR of 80 reserves 20% free space per page, reducing split frequency. In Azure SQL Database, index rebuilds can be performed online (with the ONLINE=ON option) to minimize downtime, and the ALTER INDEX REBUILD command is used to apply the new fill factor.

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: Rebuild the nonclustered index on OrderDate with a FILLFACTOR of 80. — Rebuilding the nonclustered index on OrderDate with a FILLFACTOR of 80 reduces page splits by leaving free space in each leaf-level page. This accommodates future insertions and updates that modify the OrderDate values, lowering fragmentation and improving query performance for date-based filters.

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: "most likely", "primary". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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