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AZ-204 Develop Azure compute solutions Practice Question

You are developing a web application that processes images uploaded by users. The images must be resized and analyzed for offensive content before being stored. You need to implement the solution with minimal latency and cost. What should you do?

⚠ Common exam trap

The trap here is that candidates often over-engineer the solution by choosing orchestration tools like Durable Functions or Logic Apps for simple sequential tasks, missing that a single Azure Function triggered by Blob Storage is the simplest, lowest-latency, and most cost-effective approach for event-driven image processing.

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

Use an Azure Function triggered by Blob Storage, with Consumption plan.

Using an Azure Function triggered by Blob Storage on a Consumption plan provides a serverless, event-driven architecture that automatically scales to process each image upload with minimal latency and cost. The Consumption plan charges only for execution time and resources used, making it cost-effective for sporadic workloads, while the Blob Storage trigger ensures immediate processing upon upload without polling or additional 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.

  • Use Azure Batch to process images in parallel.

    Why it's wrong here

    Azure Batch is primarily designed for large-scale, high-performance computing (HPC) workloads and scheduled batch processing jobs, not for real-time, event-driven processing of individual image uploads from a web application. Setting up and managing compute node pools for each image upload introduces significant latency and operational overhead, making it an inefficient and overly complex solution for this specific use case. Its cost model and infrastructure are optimized for long-running, parallelizable tasks rather than immediate, reactive responses.

  • Use Durable Functions to orchestrate the resizing and analysis.

    Why it's wrong here

    Durable Functions are best suited for orchestrating complex, long-running, and stateful workflows, such as fan-out/fan-in patterns, human interaction, or sequential processing with checkpoints. For a typical image processing task like resizing and analysis, the operations are usually stateless and can be completed within a single, short-lived function execution. Introducing Durable Functions adds unnecessary complexity and overhead for state management and orchestration when a simpler, stateless Azure Function can efficiently handle the entire process.

  • Use Azure Logic Apps with a trigger for each upload.

    Why it's wrong here

    Azure Logic Apps provide a visual designer for integrating services and orchestrating workflows, which can be triggered by Blob Storage uploads. However, for high-volume, compute-intensive tasks like image processing, Logic Apps can introduce higher latency and incur greater costs compared to custom code running in Azure Functions. Their per-action pricing model and managed connector overhead make them less cost-effective and performant for custom code execution than a serverless function specifically optimized for compute.

  • Use an Azure Function triggered by Blob Storage, with Consumption plan.

    Why this is correct

    An Azure Function triggered by Blob Storage on a Consumption plan is an ideal solution because it is inherently event-driven, reacting immediately to new image uploads. The Consumption plan offers a serverless execution model, meaning you only pay for the compute resources and execution time consumed, making it highly cost-effective for intermittent or variable workloads. Furthermore, it automatically scales out or in based on demand, ensuring high availability and performance without manual intervention, perfectly suiting a web application's dynamic image processing needs.

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

Senior Network & Security Engineer · founder of Courseiva

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