CV0-004 Operations and Support Practice Question
A cloud administrator is configuring auto-scaling for a batch processing application that uses an SQS queue. The number of jobs varies unpredictably. Which metric is most appropriate for scaling the worker instances?
⚠ Common exam trap
CV0-004 often tests the tendency to choose CPU utilization as a default scaling metric, but for queue-based workloads, the queue depth is a more direct and effective 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
✓
SQS queue depth (ApproximateNumberOfMessages)
SQS queue depth (ApproximateNumberOfMessages) is the most appropriate metric because it directly reflects the backlog of work. For a batch processing application with unpredictable job volumes, scaling based on queue depth ensures that worker instances are added when there are many messages waiting and removed when the queue is empty. This provides responsive scaling that matches the actual workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Memory utilization of workers
Why it's wrong here
Memory utilisation tracks resident data, not queue depth, so it stays flat while jobs accumulate and cannot drive timely scaling. It is tempting because memory is the standard scaling metric for cache or in-memory workloads, where footprint does track demand.
- ✓
SQS queue depth (ApproximateNumberOfMessages)
Why this is correct
Queue depth directly reflects pending work, so workers scale with actual backlog rather than a proxy. Because job volume varies unpredictably, ApproximateNumberOfMessages lets auto-scaling add instances when messages accumulate and remove them when the queue drains, matching capacity to demand.
- ✗
Network throughput
Why it's wrong here
Network throughput measures bytes transferred, not the number of pending jobs, so it misrepresents queue backlog and scales on the wrong signal. It is tempting because throughput suits streaming or data-transfer workloads, where volume genuinely indicates required capacity.
- ✗
CPU utilization of workers
Why it's wrong here
CPU may not always correlate with queue backlog, especially if workers are I/O bound.
About these practice questions
One of 834 original CV0-004 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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
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