AZ-204 Develop Azure compute solutions Practice Question
You have an Azure Function app that uses an Event Hubs trigger. The function processes events in batches. You notice that some events are being processed more than once. Which setting should you adjust to minimize duplicate processing?
⚠ Common exam trap
Watch out — candidates often confuse retry policies or batch sizes with the checkpointing mechanism, not realizing that duplicate processing in Event Hubs is typically caused by missing or infrequent checkpointing, not by event handling failures.
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
✓
Enable checkpointing in the function code
Checkpointing in Azure Event Hubs stores the offset of the last successfully processed event in a durable store (e.g., Azure Blob Storage). When the function restarts or scales, it resumes from that checkpoint, preventing reprocessing of already-handled events. Without checkpointing, the default behavior may start from the earliest offset or use the `latest` position, leading to duplicate 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.
- ✗
Increase the maxRetries per event
Why it's wrong here
Increasing the maxRetries per event configures how many times a single function instance will attempt to process an event or batch if an error occurs during its execution. While this helps with transient failures within a specific invocation, it does not prevent the Event Hubs trigger from delivering the same events to another function instance or re-delivering them to the same instance after a host restart or scale-out event if checkpointing is not properly managed. This setting primarily addresses processing reliability within a single execution attempt, not overall idempotency or "at-most-once" delivery guarantees across the distributed system.
- ✓
Enable checkpointing in the function code
Why this is correct
Enabling checkpointing in the function code is crucial for ensuring "at-least-once" processing and preventing duplicate event reprocessing. Checkpointing involves recording the offset of the last successfully processed event for each partition in a durable store, typically Azure Storage. When a function instance restarts, scales out, or a new instance takes over a partition, it retrieves the last checkpointed offset and begins processing events from that point, effectively preventing the re-delivery of already processed events. This mechanism is fundamental for managing distributed Event Hubs consumers.
- ✗
Increase the event batch size
Why it's wrong here
Increasing the event batch size means the function processes more events in a single invocation. If a failure occurs after some events in the batch have been processed but before the entire batch is successfully checkpointed, all events in that batch, including those already processed, will be re-delivered. This can significantly increase the number of duplicate events that need to be handled by downstream systems, as the checkpoint will only advance upon successful processing of the entire larger batch.
- ✗
Decrease the prefetch count
Why it's wrong here
Decreasing the prefetch count controls how many events the Event Hubs client proactively fetches from the Event Hub partition and buffers locally, awaiting processing. A lower prefetch count can reduce memory consumption and potentially latency for the first event, but it generally reduces overall throughput by increasing the frequency of network calls to fetch new events. This setting has no direct impact on preventing duplicate event processing across function instances or after failures, as it only concerns the client-side buffering strategy.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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