Question 904 of 161
AZ-305 Design infrastructure solutions Practice Question
A company wants to deploy containerized microservices on Azure without managing virtual machines. The solution must support automatic scaling based on demand, built-in load balancing, rolling updates for zero-downtime deployments, and a fully managed platform. Which Azure compute service should they choose?
⚠ Common exam trap
Many candidates confuse Azure Container Instances (ACI) with a managed orchestration solution, but ACI lacks the automatic scaling, load balancing, and rolling update capabilities that Container Apps provides for microservices.
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
✓
Azure Container Apps
Azure Container Apps is the correct choice because it provides a fully managed, serverless platform for running containerized microservices without managing virtual machines. It supports automatic scaling based on HTTP traffic or events, built-in load balancing via Envoy, and rolling updates with revision management to ensure zero-downtime deployments. This aligns perfectly with the requirement for a fully managed platform that abstracts away infrastructure.
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 Container Apps
Why this is correct
Azure Container Apps is a serverless platform for running containers. It provides automatic scaling based on HTTP traffic or events, built-in load balancing, and supports rolling updates via revisions. It abstracts away underlying infrastructure, so no VMs to manage.
- ✗
Azure Container Instances
Why it's wrong here
Azure Container Instances (ACI) launches a single container or a small container group in seconds, but it lacks the managed orchestration services of a microservices platform: no built-in HTTP-based autoscaling, no service discovery, no load balancing across replicas, and no revision-based rolling updates. It is better suited for isolated tasks or ephemeral burst workloads than for a multi-service application that must scale each service independently and update safely.
- ✗
Azure Batch
Why it's wrong here
Azure Batch is a job-scheduling and pool-management service designed for large-scale parallel and high-performance computing, where tasks are queued and executed across a configurable pool of virtual machines. It has no native concepts for serving long-running microservices such as per-service ingress, rolling update strategies, or automatic horizontal scaling driven by HTTP traffic, so it forces an architecture of separate batch queues and worker pools instead of continuous service-to-service communication.
- ✗
Azure Functions
Why it's wrong here
Azure Functions is a serverless execution environment driven by discrete triggers such as HTTP requests, queue messages, and timers, and it is optimized for short-running, single-purpose compute rather than for hosting containerized microservices with traffic forwarding, sidecars, and revision-level rollout control. Even when custom containers are used, the underlying model remains event-driven and stateless, which means adopting it for a microservice workload requires refactoring to a function-per-operation style and losing the container-native lifecycle automation that Container Apps offers.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 11, 2026
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