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AZ-204 Practice Question: Connect to and consume Azure services and third-party services

A company uses Azure Functions to process messages from Azure Service Bus. The function needs to scale out during high load. Which consumption plan should you choose to enable automatic scaling?

⚠ Common exam trap

It's easy for candidates to confuse the Premium plan's automatic scaling with the Consumption plan's scaling, but the question explicitly asks for the plan that 'enables automatic scaling' in the context of serverless, and the Consumption plan is the foundational serverless plan with automatic scale-out, while Premium adds features like VNet integration and pre-warmed instances.

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

Consumption plan

The Consumption plan is the correct choice because it automatically scales out based on the number of incoming messages from Azure Service Bus, adding function instances up to a maximum of 200 instances per function app. This plan is event-driven and provides true serverless scaling with no reserved capacity, making it ideal for handling variable workloads like Service Bus message processing.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Logic Apps plan

    Why it's wrong here

    The Logic Apps plan is incorrect because Azure Logic Apps is a distinct, separate integration service designed for workflow orchestration, not a hosting plan for Azure Functions. While Logic Apps can invoke Azure Functions, a Function App itself must be deployed to a specific Azure Functions hosting plan, such as Consumption, Premium, or App Service, to execute its code. Therefore, selecting a 'Logic Apps plan' as a hosting option for an Azure Function is fundamentally misidentifying the service.

  • Premium plan

    Why it's wrong here

    The Premium plan, while offering automatic scaling and pre-warmed instances for reduced cold starts, does not provide the pure, event-driven, scale-to-zero model implied by a "consumption plan" for dynamic workloads like Service Bus message processing. It is tempting because it offers enhanced performance and VNet integration, making it suitable for scenarios requiring predictable performance, dedicated resources, or private network access, where the overhead of maintaining minimum instances is acceptable.

  • Consumption plan

    Why this is correct

    The Consumption plan is the correct choice as it offers a truly serverless, event-driven execution model perfectly suited for processing messages from Azure Service Bus. This plan automatically scales resources dynamically from zero instances up to many, based on the volume of incoming messages or other event triggers, and you are billed only for the compute resources consumed during execution. This automatic, granular scaling ensures optimal cost efficiency and responsiveness for intermittent or variable workloads.

  • App Service plan

    Why it's wrong here

    The App Service plan is incorrect because, while it can host Azure Functions, its scaling mechanism is not inherently dynamic based on queue length in the same way as the Consumption plan. An App Service plan requires manual scaling or the configuration of explicit autoscale rules based on metrics like CPU usage or HTTP queue length, rather than the intrinsic, event-driven scaling provided by the Functions runtime. It also incurs costs for provisioned instances even when idle, making it less cost-effective for purely event-driven, scale-to-zero workloads.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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Written by Johnson Ajibi, MSc IT Security

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