- A
The issue is due to CPU credit exhaustion on the T3 instance. Change the DB instance class from db.t3.medium to a dedicated CPU instance type such as db.r5.large.
Why wrong: The database instance is already db.r5.large, which is a dedicated CPU instance type. CPU credit exhaustion applies only to burstable T-series instances, so this option is incorrect.
- B
The issue is due to a memory bottleneck causing queries to spill to disk. Increase the instance memory by moving to a db.r5.xlarge instance.
Why wrong: Memory usage is not indicated as a bottleneck; the instance has 16 GB memory and queries are simple SELECTs without heavy sorting or joins. ReadIOPS is below provisioned, so disk spills are unlikely.
- C
The issue is caused by a connection pool exhaustion. Increase the max_connections parameter and use connection pooling.
Why wrong: DatabaseConnections are well within the max_connections limit of 200, and connection pool exhaustion would typically cause connection timeouts, not latency spikes on simple queries.
- D
The issue is caused by a missing index on the 'orders' table. Add a composite index on the columns used in the WHERE clauses.
Correct. Despite existing indexes, a composite index can dramatically reduce latency for queries filtering on multiple columns, especially for a large table. The latency spike from 5 ms to over 500 ms suggests a full table scan or inefficient index usage, which a composite index would resolve.
DBS-C01 Composite Index Practice Question
This DBS-C01 practice question tests your understanding of monitoring and troubleshooting. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: composite Index. 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 are managing an Amazon RDS for PostgreSQL Multi-AZ DB instance that handles a high-traffic e-commerce application. Recently, the database has been experiencing intermittent slowdowns during peak hours. You have enabled Enhanced Monitoring and Performance Insights. After reviewing the Performance Insights dashboard, you notice that the 'db.sql.queries.avg_latency' metric spikes during the slowdowns, and the top SQL queries are all simple SELECT statements on a frequently accessed 'orders' table. The table has over 10 million rows and is indexed on 'order_id', 'customer_id', and 'order_date'. The average query latency for these SELECT statements jumps from 5 ms to over 500 ms during the spikes. You also observe that the 'ReadIOPS' metric on the DB instance is consistently below the provisioned IOPS limit of the gp2 storage. The DB instance type is db.r5.large with 16 GB memory. The 'DatabaseConnections' metric shows that the number of connections is well within the max_connections limit (set to 200). However, the 'CPUCreditBalance' for the underlying EC2 instance, which is a T3 medium, drops to near zero during the spikes. The 'CPUUtilization' metric is below 50%. Which of the following is the MOST likely cause and the appropriate action to resolve the issue?
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.
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
The issue is caused by a missing index on the 'orders' table. Add a composite index on the columns used in the WHERE clauses.
The issue is caused by a missing composite index on the 'orders' table. Although single-column indexes exist on 'order_id', 'customer_id', and 'order_date', the simple SELECT statements likely filter on multiple columns simultaneously, forcing the database to use a single index and then perform additional filtering in memory, resulting in high latency. Adding a composite index on the columns used in the WHERE clauses (e.g., (customer_id, order_date)) allows index-only scans and eliminates unnecessary processing. The other options are inconsistent with the symptoms: the instance is already a dedicated CPU type (db.r5.large), so CPU credit exhaustion does not apply; memory and connection metrics show no bottleneck.
Key principle: Composite Index
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The issue is due to CPU credit exhaustion on the T3 instance. Change the DB instance class from db.t3.medium to a dedicated CPU instance type such as db.r5.large.
Why it's wrong here
The database instance is already db.r5.large, which is a dedicated CPU instance type. CPU credit exhaustion applies only to burstable T-series instances, so this option is incorrect.
- ✗
The issue is due to a memory bottleneck causing queries to spill to disk. Increase the instance memory by moving to a db.r5.xlarge instance.
Why it's wrong here
Memory usage is not indicated as a bottleneck; the instance has 16 GB memory and queries are simple SELECTs without heavy sorting or joins. ReadIOPS is below provisioned, so disk spills are unlikely.
- ✗
The issue is caused by a connection pool exhaustion. Increase the max_connections parameter and use connection pooling.
Why it's wrong here
DatabaseConnections are well within the max_connections limit of 200, and connection pool exhaustion would typically cause connection timeouts, not latency spikes on simple queries.
- ✓
The issue is caused by a missing index on the 'orders' table. Add a composite index on the columns used in the WHERE clauses.
Why this is correct
Correct. Despite existing indexes, a composite index can dramatically reduce latency for queries filtering on multiple columns, especially for a large table. The latency spike from 5 ms to over 500 ms suggests a full table scan or inefficient index usage, which a composite index would resolve.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Composite Index
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may overlook that single-column indexes are not always sufficient for multi-column predicates. Even with indexes on individual columns, queries may still perform poorly without a composite index covering all filter columns.
Detailed technical explanation
How to think about this question
T3 instances use a CPU credit mechanism where each credit provides one minute of 100% CPU utilization; when credits are exhausted, the instance is throttled to the baseline (e.g., 10% for t3.medium). Even at 50% CPU utilization, the instance may be starved for credits if the workload is bursty, causing intermittent latency spikes. Performance Insights shows 'db.sql.queries.avg_latency' spikes because the CPU cannot process queries quickly enough, despite low ReadIOPS and sufficient memory.
KKey Concepts to Remember
- Composite Index
- Index-Only Scan
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
Composite Index
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Visual reference
What to study next
Got this wrong? Here's your next step.
Review composite Index, then practise related DBS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Monitoring and Troubleshooting — This question tests Monitoring and Troubleshooting — Composite Index.
What is the correct answer to this question?
The correct answer is: The issue is caused by a missing index on the 'orders' table. Add a composite index on the columns used in the WHERE clauses. — The issue is caused by a missing composite index on the 'orders' table. Although single-column indexes exist on 'order_id', 'customer_id', and 'order_date', the simple SELECT statements likely filter on multiple columns simultaneously, forcing the database to use a single index and then perform additional filtering in memory, resulting in high latency. Adding a composite index on the columns used in the WHERE clauses (e.g., (customer_id, order_date)) allows index-only scans and eliminates unnecessary processing. The other options are inconsistent with the symptoms: the instance is already a dedicated CPU type (db.r5.large), so CPU credit exhaustion does not apply; memory and connection metrics show no bottleneck.
What should I do if I get this DBS-C01 question wrong?
Review composite Index, then practise related DBS-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". 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?
Composite Index
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Last reviewed: Jun 11, 2026
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