Quick Answer
The answer is to create a covering index that includes all columns referenced in the query. This is correct because the slow performance stems from key lookups, which force random I/O to fetch additional columns from the clustered index after the non-clustered index filters rows. By building a covering index that contains every column needed by the query—both the filter and the SELECT columns—you eliminate those lookups entirely, turning the operation into a single, efficient index seek or scan that drastically reduces page reads. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of index design trade-offs, often appearing as a performance-tuning question where a common trap is to suggest adding more non-clustered indexes or rebuilding statistics instead. Remember the memory tip: “Cover the query, skip the lookup”—if your index covers all referenced columns, no extra I/O is needed.
This PCDE practice question tests your understanding of monitor and optimize database performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Your Cloud SQL for SQL Server instance has a query that uses a non-clustered index to filter rows but then performs key lookups to retrieve additional columns. The query is slow. Which database tuning option would most likely reduce I/O?
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.
Increase the buffer pool size
Why wrong: Doesn't reduce the number of key lookups.
Rebuild the non-clustered index with FILLFACTOR=80
Why wrong: Doesn't change the index structure to cover the query.
Create a covering index that includes all columns referenced in the query
Covering index avoids the need for lookups, reducing I/O.
Use a FORCESEEK query hint
Why wrong: May force an index but still key lookups occur.
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 that includes all columns referenced in the query
The query is slow because key lookups require random I/O to retrieve additional columns not included in the non-clustered index. Creating a covering index that includes all columns referenced in the query eliminates the need for key lookups entirely, converting the operation into a single index seek or scan with minimal I/O. This directly reduces the number of page reads and improves query performance.
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.
Increase the buffer pool size
Why it's wrong here
Doesn't reduce the number of key lookups.
Rebuild the non-clustered index with FILLFACTOR=80
Why it's wrong here
Doesn't change the index structure to cover the query.
Create a covering index that includes all columns referenced in the query
Why this is correct
Covering index avoids the need for lookups, reducing I/O.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use a FORCESEEK query hint
Why it's wrong here
May force an index but still key lookups occur.
Common exam traps
Google Cloud often tests the misconception that any index tuning or query hint can fix performance, but the trap here is that candidates may choose FORCESEEK or FILLFACTOR without realizing that only a covering index directly addresses the root cause of key lookup I/O.
Detailed technical explanation
A covering index is a non-clustered index that includes all columns needed by the query, either as key columns or as included columns (using the INCLUDE clause in SQL Server). Under the hood, when the index is covering, the query engine can satisfy the query entirely from the index leaf pages without needing to access the clustered index or heap, eliminating costly bookmark lookups. In real-world scenarios, this is especially impactful for queries that filter on a narrow set of columns but select many additional columns, as the I/O savings can be dramatic.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
The correct answer is: Create a covering index that includes all columns referenced in the query — The query is slow because key lookups require random I/O to retrieve additional columns not included in the non-clustered index. Creating a covering index that includes all columns referenced in the query eliminates the need for key lookups entirely, converting the operation into a single index seek or scan with minimal I/O. This directly reduces the number of page reads and improves query performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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