The answer is a non-clustered index on the LastLoginDate column. This is correct because a non-clustered index on a date column in Azure SQL Database creates a separate B-tree structure that allows the query engine to perform an efficient range scan, quickly locating rows where LastLoginDate is older than one year without scanning the entire 10-million-row table. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of index types for query optimization, specifically how non-clustered indexes accelerate filtering on non-key columns. A common trap is assuming a clustered index on the primary key suffices, but that only speeds up lookups by CustomerID, not date-based range queries. Remember the memory tip: "Date filter? Non-clustered is the sifter."
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 a customer management system. The Customers table has columns: CustomerID (int, primary key), FullName (varchar(100)), Email (varchar(200)), SignUpDate (date), LastLoginDate (date). Queries frequently filter on LastLoginDate to find customers who have not logged in for over a year for a promotional campaign. The table has 10 million rows. Which type of index should they create to optimize these queries?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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.
Why wrong: This index already exists as the primary key and is not useful for filtering on LastLoginDate. It organizes data by CustomerID, not by date.
B
Non-clustered index on LastLoginDate
A non-clustered index on LastLoginDate enables the database to efficiently seek rows with a specific date range, significantly speeding up the query.
C
Non-clustered index on FullName
Why wrong: FullName is not used in the filter condition, so this index would not help the query performance.
D
Columnstore index on SignUpDate and LastLoginDate
Why wrong: Columnstore indexes are designed for large-scale analytical queries involving aggregations, not for simple range lookups on a single table. They would not improve this type of query.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Non-clustered index on LastLoginDate
A non-clustered index on LastLoginDate allows the query to quickly locate rows where LastLoginDate is older than one year without scanning the entire 10-million-row table. Azure SQL Database uses B-tree structures for non-clustered indexes, enabling efficient range scans and key lookups for the filtered rows. This directly supports the promotional campaign query pattern.
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.
✗
Clustered index on CustomerID
Why it's wrong here
This index already exists as the primary key and is not useful for filtering on LastLoginDate. It organizes data by CustomerID, not by date.
✓
Non-clustered index on LastLoginDate
Why this is correct
A non-clustered index on LastLoginDate enables the database to efficiently seek rows with a specific date range, significantly speeding up the query.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Non-clustered index on FullName
Why it's wrong here
FullName is not used in the filter condition, so this index would not help the query performance.
✗
Columnstore index on SignUpDate and LastLoginDate
Why it's wrong here
Columnstore indexes are designed for large-scale analytical queries involving aggregations, not for simple range lookups on a single table. They would not improve this type of query.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a clustered index on the primary key by default, failing to recognize that the query predicate (LastLoginDate) is not the clustering key, so the index cannot be used to efficiently filter the data.
Detailed technical explanation
How to think about this question
Under the hood, the non-clustered index on LastLoginDate stores the indexed column values along with row locators (either the clustered index key or a row ID). For a query like 'SELECT * FROM Customers WHERE LastLoginDate < DATEADD(year, -1, GETDATE())', SQL Server performs an index seek to find the first qualifying row, then scans forward or backward through the leaf pages. This avoids scanning all 10 million rows, reducing I/O from potentially millions of pages to just the index pages covering the filtered range.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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: Non-clustered index on LastLoginDate — A non-clustered index on LastLoginDate allows the query to quickly locate rows where LastLoginDate is older than one year without scanning the entire 10-million-row table. Azure SQL Database uses B-tree structures for non-clustered indexes, enabling efficient range scans and key lookups for the filtered rows. This directly supports the promotional campaign query pattern.
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: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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