AZ-305 Design infrastructure solutions Practice Question
You are designing a microservices architecture on Azure Kubernetes Service (AKS). The solution must handle traffic spikes by automatically scaling pods based on CPU utilization. Additionally, you need to minimize cost by scaling down nodes when not in use. Which two features should you implement? (Choose two.)
⚠ Common exam trap
Candidates often confuse Horizontal Pod Autoscaler (which scales pods) with Cluster Autoscaler (which scales nodes), or mistakenly think that a load balancer or ACI can handle the scaling requirements directly, but the question explicitly requires both pod-level and node-level scaling for cost minimization.
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
✓
Horizontal Pod Autoscaler (HPA)
Horizontal Pod Autoscaler (HPA) automatically scales the number of pod replicas based on observed CPU utilization (or custom metrics), directly addressing the requirement to handle traffic spikes by scaling pods. Cluster Autoscaler automatically adjusts the number of AKS nodes by scaling down unused nodes and scaling up when pods are unschedulable, which minimizes cost by reducing node count during low usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Load Balancer
Why it's wrong here
Azure Load Balancer is a Layer 4 load balancer that distributes inbound traffic to healthy backend instances. It does not evaluate resource utilization metrics or make scaling decisions. Autoscaling requires a control loop that reads metrics and adjusts replica counts, which the load balancer does not provide; it merely routes traffic.
- ✓
Horizontal Pod Autoscaler (HPA)
Why this is correct
Horizontal Pod Autoscaler is a Kubernetes control loop that queries the Metrics API for CPU, memory, or custom application metrics and automatically updates the replica count of a Deployment or ReplicaSet. This scaling mechanism is fundamental to microservices on AKS because it dynamically matches the number of running pod instances to the observed demand, ensuring high availability and cost efficiency without manual intervention.
- ✗
Vertical Pod Autoscaler (VPA)
Why it's wrong here
Vertical Pod Autoscaler works by analyzing historical resource usage and then adjusting the CPU and memory requests and limits of the containers within existing pods, after which it may restart those pods. While it optimizes individual pod resource allocation and can improve utilization, it does not increase the number of pod replicas. For scaling out a microservice horizontally, HPA is the appropriate mechanism.
- ✗
Azure Container Instances (ACI)
Why it's wrong here
Azure Container Instances is a separate serverless container service that runs containers on demand without managing underlying VMs. In an AKS context, virtual nodes can schedule burstable pods to ACI, but this is not a native AKS autoscaling feature; it is an alternative scheduling target. ACI itself does not modify the replica count of a Kubernetes workload, so it cannot serve as the autoscaler for the microservice.
- ✓
Cluster Autoscaler
Why this is correct
Cluster Autoscaler is an AKS component that automatically resizes the node pool by adding or removing nodes based on pending pods that cannot be scheduled due to insufficient cluster capacity. It operates at the infrastructure layer and complements HPA, which handles the pod-level replica count. For a specific microservice, HPA is the primary autoscaler because it directly controls pod replicas, while Cluster Autoscaler addresses cluster-wide resource constraints.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-305 practice question is part of Courseiva's free Microsoft 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 AZ-305 exam.