CV0-004 Cloud Architecture and Design Practice Question
A cloud architect is designing a containerized microservices platform for a retail company. The company requires that individual services scale independently, that failed containers be replaced automatically without manual intervention, and that the platform abstract away the underlying compute hosts. The operations team has limited experience with cluster management. Which cloud-native orchestration approach BEST meets these requirements?
⚠ Common exam trap
The trap here is assuming that autoscaling groups of virtual machines provide the same host abstraction and self-healing as a container orchestrator.
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
✓
Deploy the microservices to a managed Kubernetes service and define Deployments and Horizontal Pod Autoscalers.
A managed Kubernetes service supplies the orchestration features the scenario demands: scheduling across hosts, self-healing of failed containers via controllers, and per-service scaling through Horizontal Pod Autoscalers. It also removes control-plane administration from a team lacking deep cluster skills. The other approaches either fail to abstract hosts, prevent independent scaling, or impose execution-model limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy the microservices to a managed Kubernetes service and define Deployments and Horizontal Pod Autoscalers.
Why this is correct
A managed Kubernetes service provides a control plane that schedules containers, restarts failed pods through the ReplicaSet controller, and abstracts the worker nodes. Horizontal Pod Autoscalers adjust replica counts per service based on metrics, so each microservice scales independently. Because the provider manages the control plane, the operations team's limited cluster expertise is less of an obstacle.
- ✗
Deploy each microservice on a dedicated virtual machine with an autoscaling group per service.
Why it's wrong here
Dedicated VMs with per-service autoscaling groups can scale and replace failed instances, but they do not abstract away compute hosts and require significant manual cluster and configuration management. This increases operational burden on a team with limited cluster experience, and packing microservices one-per-VM wastes resources and slows deployment compared with a container orchestrator.
- ✗
Package all microservices into a single monolithic container and run it on one large virtual machine.
Why it's wrong here
Combining all services into one container recreates a monolith, so services cannot scale or fail independently. A single VM also introduces a single point of failure and provides no automatic container replacement or host abstraction. This directly contradicts the requirement for independent scaling and automated recovery, making it unsuitable despite its apparent simplicity.
- ✗
Use serverless functions for every service and rely on the provider to manage all scaling and placement.
Why it's wrong here
Serverless functions do scale automatically and remove host management, but they impose execution time limits, cold-start latency, and event-driven invocation models that poorly fit long-running or stateful microservices. Not every service maps cleanly to a function, so this approach cannot satisfy the general containerized microservices requirement as reliably as an orchestrator.
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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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