DVA-C02 Troubleshooting and Optimization Practice Question
An application running on Amazon EC2 instances behind an Application Load Balancer (ALB) is experiencing increased latency. The developer suspects the ALB is the bottleneck. How can the developer confirm this using CloudWatch metrics?
⚠ Common exam trap
AWS frequently tests your ability to distinguish between Classic Load Balancer (CLB) metrics (like SurgeQueueLength and SpilloverCount) and Application Load Balancer (ALB) metrics (like TargetResponseTime). Remember that ALBs do not have a request queue metric.
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 the TargetResponseTime metric and compare it to the client's perspective.
To determine if the ALB or the backend targets are causing the latency, the developer should monitor the 'TargetResponseTime' metric. This metric measures the time elapsed (in seconds) from when the request leaves the ALB until a response from the target is received. If 'TargetResponseTime' is low but the client-side latency is high, the bottleneck lies within the ALB itself or the network. If 'TargetResponseTime' is high, the bottleneck is the backend EC2 instances. 'SurgeQueueLength' is a Classic Load Balancer metric and is not available on ALBs.
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 the HealthyHostCount metric and ensure it is equal to the number of instances.
Why it's wrong here
The HealthyHostCount metric indicates the number of healthy targets registered with the Application Load Balancer (ALB) that are available to receive requests. While crucial for overall application availability, this metric does not directly reflect latency experienced by clients due to the ALB itself. A full count of healthy hosts only confirms backend availability, not whether the ALB is efficiently processing and forwarding requests without internal queuing delays.
- ✗
Monitor the SurgeQueueLength metric and look for sustained high values.
Why it's wrong here
The SurgeQueueLength metric represents the total number of requests currently queued by the Application Load Balancer (ALB) because it cannot immediately forward them to a healthy registered target. Sustained high values for SurgeQueueLength are a direct indicator that the ALB is experiencing a bottleneck, leading to increased client-perceived latency as requests wait in the queue before being processed. This metric specifically captures delays occurring within the ALB.
- ✓
Monitor the TargetResponseTime metric and compare it to the client's perspective.
Why this is correct
The TargetResponseTime metric measures the time elapsed from when the Application Load Balancer (ALB) sends a request to a registered target until the target responds. While important for understanding backend performance, this metric only accounts for the time after the request leaves the ALB. It does not include any potential delays or queuing time the request might experience within the ALB before being forwarded, thus not fully representing the client's total perceived latency.
- ✗
Monitor the RequestCount metric and check if it exceeds the ALB's limit.
Why it's wrong here
The RequestCount metric tracks the total number of requests processed by the Application Load Balancer (ALB) over a specified period. While useful for monitoring overall traffic volume and throughput, this metric does not directly indicate latency experienced by clients. A high RequestCount alone doesn't mean requests are delayed; it simply shows demand. Latency issues would be reflected in other metrics, such as SurgeQueueLength, if the ALB or targets are unable to keep up with the request volume.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DVA-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DVA-C02 exam.