AZ-204 Develop Azure compute solutions Practice Question
A containerized checkout API deployed to Azure Container Apps must scale to zero when idle and scale out based on queue length. What should the developer configure?
⚠ Common exam trap
Candidates often confuse Azure Container Apps' built-in HTTP scaling rules with KEDA-based event-driven scaling, or incorrectly assume that a manual replica count or a load-balancing health probe can achieve the required queue-based autoscaling behavior.
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
✓
A KEDA-based scale rule for the queue trigger
Azure Container Apps supports KEDA (Kubernetes Event-Driven Autoscaling) for scaling based on external metrics. A KEDA-based scale rule configured with an Azure Queue Storage trigger allows the containerized checkout API to scale to zero when no messages are in the queue and scale out dynamically as queue length increases, meeting the requirement precisely.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A KEDA-based scale rule for the queue trigger
Why this is correct
This is the correct approach for event-driven scaling in Azure Container Apps. KEDA (Kubernetes Event-driven Autoscaling) integrates directly with Container Apps, allowing it to monitor external event sources like Azure Storage Queues. A KEDA-based scale rule would specify the queue to monitor and define thresholds (e.g., messages per replica) that trigger scaling actions, ensuring the API scales out when queue length increases and scales in (potentially to zero) when the queue is empty. This efficiently handles fluctuating loads for a checkout API processing asynchronous requests.
- ✗
A manual replica count only
Why it's wrong here
Relying solely on a manual replica count for a containerized checkout API deployed to Azure Container Apps would prevent dynamic scaling. While it provides a fixed capacity, it cannot automatically adjust to changes in queue length, leading to either over-provisioning (wasting resources) or under-provisioning (causing message backlogs and performance degradation). Crucially, a manual replica count cannot scale down to zero when there are no messages in the queue, incurring unnecessary costs.
- ✗
An Availability Set
Why it's wrong here
An Availability Set is a feature designed for Azure Virtual Machines to ensure high availability by distributing VMs across different fault domains and update domains within an Azure datacenter. This concept is entirely irrelevant to Azure Container Apps, which is a fully managed serverless container platform. Container Apps inherently provides high availability and resilience through its underlying infrastructure, abstracting away the need for users to configure VM-level constructs like Availability Sets for their containerized applications.
- ✗
An Azure Front Door health probe
Why it's wrong here
An Azure Front Door health probe is used to monitor the health and availability of backend services, enabling Front Door to intelligently route client traffic to healthy instances. While essential for traffic management and ensuring requests reach a responsive API, a health probe itself does not initiate scaling actions based on application-specific metrics like the length of a message queue. Its purpose is to determine backend readiness, not to dynamically adjust the number of running container replicas in response to event-driven load.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-204 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-204 exam.