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CV0-004 Practice Question: A cloud architect is designing a deployment…
A cloud architect is designing a deployment strategy for a web application that must handle unpredictable traffic spikes. The application runs in containers on a Kubernetes cluster. The architect wants to minimize costs while ensuring that the cluster can scale out rapidly during spikes. Which deployment strategy best meets these requirements?
⚠ Common exam trap
CompTIA often tests the distinction between horizontal and vertical scaling in the context of cost and rapid elasticity; the trap here is that candidates may choose vertical autoscaling (Option D) thinking it is cheaper, but it cannot scale out quickly enough for unpredictable spikes and is limited by node resources.
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
✓
Implement horizontal pod autoscaling based on CPU utilization.
Horizontal Pod Autoscaling (HPA) automatically adjusts the number of pod replicas based on observed CPU utilization (or custom metrics), enabling rapid scale-out during traffic spikes without manual intervention. This minimizes costs by running only the necessary pods during low traffic while ensuring the cluster can react quickly to increased demand, which aligns with the requirement for unpredictable spikes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-provision a fixed number of pods to handle peak load at all times.
Why it's wrong here
Fixed pre-provisioning pays for peak capacity continuously, so idle pods inflate cost during normal traffic, violating the minimise-cost requirement. It is tempting because static provisioning guarantees instant capacity with no scaling delay, and would suit steady, predictable workloads where over-provisioning headroom is acceptable.
- ✗
Manually scale the deployment when monitoring alerts indicate high traffic.
Why it's wrong here
Manual scaling reacts only after alerts fire, so pods are added too slowly for unpredictable spikes, and an operator must be available. It is tempting because manual control avoids runaway automation costs, and would suit scheduled, forecastable load changes where an engineer can act in advance.
- ✓
Implement horizontal pod autoscaling based on CPU utilization.
Why this is correct
Horizontal pod autoscaling adds or removes pod replicas based on CPU utilisation, matching capacity to demand so the cluster scales out rapidly during spikes and shrinks afterwards. This pay-for-what-you-use elasticity minimises cost compared with over-provisioning static nodes.
- ✗
Use vertical pod autoscaling to increase resource limits on existing pods.
Why it's wrong here
Vertical pod autoscaling raises CPU and memory limits on existing pods, which requires pod restarts and cannot add replica count to absorb a traffic surge. It is tempting because it right-sizes resource requests, and would suit workloads whose individual pods are consistently under- or over-allocated.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.