Question 706 of 982

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

Quick Answer

The answer is to create a non-clustered composite index on (WarehouseID, LastUpdatedDate) with the Quantity column included. This works because a composite index on both columns allows Azure SQL Database to perform composite index filtering sorting in a single index seek, locating rows by WarehouseID while already returning them in the correct sort order of LastUpdatedDate without a separate sort operation. Including Quantity as a non-key column eliminates expensive key lookups to the clustered index, directly addressing the query’s need for that column. On the DP-900 exam, this scenario tests your understanding of covering indexes and the difference between key columns (used for filtering and sorting) and included columns (added to avoid lookups). A common trap is choosing a single-column index on LastUpdatedDate, which ignores the filter, or changing the clustered index, which would break uniqueness and cause fragmentation. Memory tip: “Composite for filter and sort, include the rest to avoid a key-lookup quest.”

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 an inventory management system. The Inventory table has millions of rows. Queries frequently filter on WarehouseID and then sort by LastUpdatedDate. The table currently has a clustered index on InventoryID (primary key). Which action will most improve query performance for these frequent filters?

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.

Question 1mediummultiple choice

Full question →

A
Create a non-clustered index on (WarehouseID, LastUpdatedDate) INCLUDE (Quantity)
This composite index covers the filter and sort conditions. Including Quantity as an included column makes the index covering for this query, avoiding expensive key lookups.
B
Add a clustered index on WarehouseID
Why wrong: Changing the clustered index to WarehouseID may cause row movement and fragmentation, as WarehouseID is likely not unique. A clustered index should ideally be unique and stable.
C
Create a non-clustered index on LastUpdatedDate only
Why wrong: This index helps sorting by LastUpdatedDate, but does not support the filter on WarehouseID. The database would still need to scan or perform lookups for the specific WarehouseID.
D
Partition the table by InventoryID
Why wrong: Partitioning by InventoryID does not align with the query pattern. Queries filtering on WarehouseID would still span multiple partitions, providing no performance benefit.

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 non-clustered index on (WarehouseID, LastUpdatedDate) INCLUDE (Quantity)

A non-clustered index on (WarehouseID, LastUpdatedDate) allows the database engine to efficiently locate rows matching a specific WarehouseID and return them already sorted by LastUpdatedDate without accessing the clustered index (or with minimal lookup). Including the Quantity column as a non-key included column avoids key lookups for that column, further improving performance. Changing the clustered index to WarehouseID could cause fragmentation and is not ideal for uniqueness. A single-column index on LastUpdatedDate does not support the filter on WarehouseID. Partitioning by InventoryID does not help this query pattern.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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 non-clustered index on (WarehouseID, LastUpdatedDate) INCLUDE (Quantity)
Why this is correct
This composite index covers the filter and sort conditions. Including Quantity as an included column makes the index covering for this query, avoiding expensive key lookups.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Add a clustered index on WarehouseID
Why it's wrong here
Changing the clustered index to WarehouseID may cause row movement and fragmentation, as WarehouseID is likely not unique. A clustered index should ideally be unique and stable.
✗
Create a non-clustered index on LastUpdatedDate only
Why it's wrong here
This index helps sorting by LastUpdatedDate, but does not support the filter on WarehouseID. The database would still need to scan or perform lookups for the specific WarehouseID.
✗
Partition the table by InventoryID
Why it's wrong here
Partitioning by InventoryID does not align with the query pattern. Queries filtering on WarehouseID would still span multiple partitions, providing no performance benefit.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Real-world example

How this comes up in practice

A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

What to study next

Got this wrong? Here's your next step.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-900 NAT questions on configuration and troubleshooting.

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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 — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Create a non-clustered index on (WarehouseID, LastUpdatedDate) INCLUDE (Quantity) — A non-clustered index on (WarehouseID, LastUpdatedDate) allows the database engine to efficiently locate rows matching a specific WarehouseID and return them already sorted by LastUpdatedDate without accessing the clustered index (or with minimal lookup). Including the Quantity column as a non-key included column avoids key lookups for that column, further improving performance. Changing the clustered index to WarehouseID could cause fragmentation and is not ideal for uniqueness. A single-column index on LastUpdatedDate does not support the filter on WarehouseID. Partitioning by InventoryID does not help this query pattern.

What should I do if I get this DP-900 question wrong?

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?

Static NAT maps one inside address to one outside address.

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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 company uses Azure SQL Database to store a large table of sales transactions with columns: TransactionID (primary key), CustomerID, ProductID, SaleDate, Amount. Queries frequently filter by both CustomerID and SaleDate to retrieve sales for a specific customer over a date range. Which indexing strategy will most improve query performance?

medium

A.Create a clustered index on SaleDate
B.Create a nonclustered index on CustomerID and include SaleDate
✓ C.Create a nonclustered index on (CustomerID, SaleDate)
D.Create a nonclustered index on (SaleDate, CustomerID)

Why C: Option C is correct because creating a nonclustered index on (CustomerID, SaleDate) as a composite index directly supports the query predicate that filters by both CustomerID and SaleDate. The index is ordered by CustomerID first, enabling efficient seeks for a specific customer, and then by SaleDate within each customer, allowing the query engine to perform a range scan for the date range without scanning the entire table or sorting. This index is a covering index for this query, as it contains all columns needed for the filter, avoiding key lookups.

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