CV0-004 Operations and Support Practice Question
A cloud administrator is setting up auto-scaling for a web application that uses an SQS queue for incoming requests. The administrator wants to scale the number of EC2 instances based on the queue depth. Which two metrics are appropriate for this auto-scaling policy? (Choose TWO.)
⚠ Common exam trap
CV0-004 often tests the temptation to scale on CPU or memory for queue-based workloads, when the correct signal is queue depth or backlog per instance.
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
✓
ApproximateNumberOfMessagesVisible (queue depth)
Option B, ApproximateNumberOfMessagesVisible (queue depth), is correct because it is the native CloudWatch metric that reports how many messages are available in the SQS queue, directly reflecting the incoming workload that the EC2 fleet must process. Option E, BacklogPerInstance (queue depth per instance), is correct because it is a custom metric that divides the queue backlog by the number of running instances, giving a per-instance workload signal that is ideal for target-tracking scaling policies on an SQS-backed web tier. Options A (CPU utilization) and C (memory utilization) are not appropriate here because the workload is driven by queue depth rather than instance-level compute or memory pressure, and memory utilization is not even a default CloudWatch EC2 metric. Option D (network throughput) is also unsuitable because it measures data transfer volume, not the amount of pending work in the queue, so it would not reliably track the backlog the application must drain.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CPU utilization of instances
Why it's wrong here
CPU utilization is not directly related to queue depth.
- ✓
ApproximateNumberOfMessagesVisible (queue depth)
Why this is correct
ApproximateNumberOfMessagesVisible reports the count of messages awaiting retrieval, directly reflecting backlog depth. Scaling on this metric adds EC2 capacity precisely when consumer throughput lags behind incoming demand, satisfying the requirement to scale on queue depth rather than CPU or network statistics.
- ✗
Memory utilization of instances
Why it's wrong here
Memory utilisation reflects instance resource consumption, not the number of messages waiting in SQS. It is tempting because memory is a common custom metric, and would be correct for scaling workloads whose bottleneck is memory pressure rather than queue backlog.
- ✗
Network throughput
Why it's wrong here
Network throughput measures data transferred by instances, not messages awaiting processing in the queue. It is tempting because throughput often correlates with load, and would be correct for scaling network-bound services where bandwidth is the limiting resource.
- ✓
BacklogPerInstance (queue depth per instance)
Why this is correct
BacklogPerInstance directly measures queue depth divided by running instances, so scaling triggers when each instance's share of pending messages rises. This satisfies the stem's requirement to scale on queue depth, unlike CPU or network metrics that lag behind actual SQS backlog growth.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This CV0-004 practice question is part of Courseiva's free CompTIA 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 CV0-004 exam.