CV0-004 Troubleshooting Practice Question
A cloud engineer is troubleshooting a performance issue where a web server cluster experiences high latency during peak hours. The cluster uses an auto-scaling group behind a load balancer. Which THREE steps should the engineer take to identify the root cause?
⚠ Common exam trap
Watch out — candidates often think reducing instances (Option D) is a valid troubleshooting step, but it is a remediation action that can mask the root cause and potentially crash the application under load.
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
✓
Monitor CPU and memory utilization on the web servers
Option A is correct because monitoring CPU and memory utilization on the web servers reveals whether the instances are resource-saturated during peak hours, which is a common cause of high latency in an auto-scaling cluster. Option B is correct because analyzing web server access logs for slow requests helps pinpoint which endpoints, queries, or upstream dependencies are contributing to the latency, providing concrete evidence of the bottleneck. Option C is correct because checking the load balancer's backend instance health status verifies whether unhealthy or failing instances are being served or whether health checks are flapping, which directly impacts response times. Option D is incorrect because reducing the number of instances in the auto-scaling group would decrease capacity and likely worsen latency rather than help diagnose the root cause. Option E is incorrect because reviewing security group rules for the load balancer addresses connectivity and access control, not performance degradation during peak hours.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Monitor CPU and memory utilization on the web servers
Why this is correct
High latency during peak hours may stem from CPU or memory saturation on the web servers themselves. Monitoring both metrics reveals whether instances are resource-constrained, which would prevent the auto-scaling group from serving requests promptly behind the load balancer.
- ✓
Analyze web server access logs for slow requests
Why this is correct
Access logs record per-request response times, exposing which endpoints or requests are slow during peak hours. Analysing them isolates whether latency originates in application processing rather than the load balancer or scaling behaviour, directly identifying the bottleneck.
- ✓
Check the load balancer's backend instance health status
Why this is correct
The load balancer only routes to backends it considers healthy. Checking backend instance health status reveals whether instances are failing health checks or being removed during peak load, which would reduce capacity and drive up latency across the cluster.
- ✗
Reduce the number of instances in the auto-scaling group
Why it's wrong here
Removing instances reduces capacity, worsening latency during peak demand rather than diagnosing it. It is tempting as a cost or scaling lever, and would be correct when deliberately scaling in during off-peak periods, not when investigating why an auto-scaling cluster responds slowly under load.
- ✗
Review security group rules for the load balancer
Why it's wrong here
Security group rules govern permitted traffic, not latency; they cause connection failures or blocks rather than slow responses under load. Reviewing them is tempting as routine troubleshooting, and would be correct when diagnosing refused or dropped connections instead of peak-hour performance degradation.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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