AZ-204 Develop Azure compute solutions Practice Question
You are developing an Azure Functions app that processes events from an Event Hubs instance. The function must scale out automatically based on the number of partitions in the Event Hub. You need to ensure that each function instance processes events from at least one partition. Which TWO configurations should you use?
⚠ Common exam trap
Test-takers frequently confuse batch size configuration (MaxBatchSize) with scaling behavior, or assume a Premium plan is mandatory for partition-level scaling, when in fact the Event Scale mode and checkpointing are the key mechanisms.
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
✓
Set the function app to use the 'Event Scale' mode with a target of one instance per partition.
The 'Event Scale' mode with a target of one instance per partition ensures that the function app scales out to match the number of Event Hub partitions, with each instance processing events from at least one partition. Option C is correct because an event processor host with blob storage for checkpointing enables load balancing across multiple instances, ensuring each instance handles one or more partitions. Option E is incorrect because 'PartitionKey' is used when sending events to Event Hubs to assign a partition, not in the trigger binding; the trigger automatically distributes partitions across instances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the function app to use the 'Event Scale' mode with a target of one instance per partition.
Why this is correct
Event Scale mode maximizes parallelism per partition.
- ✗
Set the 'MaxBatchSize' property to 1 to ensure even distribution.
Why it's wrong here
Batch size does not affect partition distribution.
- ✓
Configure the function to use an event processor host with blob storage for checkpointing.
Why this is correct
Checkpointing allows load balancing across partitions.
- ✗
Select the Premium App Service plan for the function app.
Why it's wrong here
Premium plan is not required for partition distribution.
- ✗
Use the EventHubs trigger with the 'PartitionKey' parameter set to the partition ID.
Why it's wrong here
Incorrect. The 'PartitionKey' parameter is used when sending events to specify a partition, not in the trigger binding. The Event Hubs trigger automatically distributes partitions among function instances; you do not set the partition key in the trigger.
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 |
Go deeper
Related to this question
Learn chapter
Azure Functions Development
Key term
Azure Functions Bindings
Azure Functions Bindings are declarative connections that link your serverless function code to Azure services or external resources, handling input and output data automatically without writing extra networking or authentication code.
Key term
Durable Functions
Durable Functions is an extension of Azure Functions that lets you write stateful workflows in code, managing complex sequences of tasks, retries, and delays automatically.
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