Quick Answer
The answer is to create a covering index on DateKey and ProductKey that includes the aggregated columns. This is correct because a covering index contains all columns needed by the query—both the filter predicates and the aggregation columns—allowing the query engine to retrieve results solely from the index pages, bypassing the clustered index scan entirely. On the DP-300 exam, this scenario tests your understanding of index design for large-table aggregation workloads, where a nonclustered index on DateKey alone is insufficient because it still requires key lookups to fetch the aggregated data. A common trap is to suggest adding more indexes or partitioning, but without changing the service tier, a covering index is the most direct way to improve aggregation query performance on a large table in Azure SQL Database. Remember the mnemonic: “Cover the query, skip the scan.”
This DP-300 practice question tests your understanding of monitor, configure, and optimize database resources. 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.
You manage an Azure SQL Database (General Purpose, S2) used by a reporting application. The database has a table `FactSales` with 500 million rows. Queries that aggregate sales by date are slow. The execution plan shows a clustered index scan on `FactSales`. The table has a clustered index on `SaleID` and a nonclustered index on `DateKey`. The queries filter by `DateKey` and `ProductKey`. You need to improve query performance without changing the service tier. Which action should you take?
Create a nonclustered index on ProductKey only.
Why wrong: ProductKey index alone does not cover the query.
Partition the table by DateKey.
Why wrong: Partitioning does not guarantee index seeks.
Create a clustered columnstore index on the table.
Why wrong: Columnstore is for DW, not OLTP.
Create a covering index on DateKey and ProductKey including the aggregated columns.
Covering index provides index seeks and avoids lookups.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Create a covering index on DateKey and ProductKey including the aggregated columns.
The query filters by DateKey and ProductKey and aggregates sales data. A covering index on DateKey and ProductKey that includes the aggregated columns (e.g., SUM(SalesAmount)) allows the query to be satisfied entirely from the index without touching the clustered index, eliminating the costly clustered index scan. This is the most direct and effective way to improve performance without changing the service tier.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Create a nonclustered index on ProductKey only.
Why it's wrong here
ProductKey index alone does not cover the query.
Partition the table by DateKey.
Why it's wrong here
Partitioning does not guarantee index seeks.
Create a clustered columnstore index on the table.
Why it's wrong here
Columnstore is for DW, not OLTP.
Create a covering index on DateKey and ProductKey including the aggregated columns.
Why this is correct
Covering index provides index seeks and avoids lookups.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may choose partitioning (Option B) thinking it speeds up all queries by date, but without a covering index, partitioning alone does not eliminate the scan; it only reduces the data scanned to a single partition.
Detailed technical explanation
A covering index includes all columns referenced in the SELECT, WHERE, and JOIN clauses, allowing the query optimizer to perform an index-only scan (no key lookups). In SQL Server, this is achieved by using the INCLUDE clause to add non-key columns to a nonclustered index. For aggregation queries, the index can also be ordered to support GROUP BY operations efficiently, reducing the need for a sort operator.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Monitor, configure, and optimize database resources — This question tests Monitor, configure, and optimize database resources — Read the scenario before looking for a memorised answer..
The correct answer is: Create a covering index on DateKey and ProductKey including the aggregated columns. — The query filters by DateKey and ProductKey and aggregates sales data. A covering index on DateKey and ProductKey that includes the aggregated columns (e.g., SUM(SalesAmount)) allows the query to be satisfied entirely from the index without touching the clustered index, eliminating the costly clustered index scan. This is the most direct and effective way to improve performance without changing the service tier.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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