- A
Create a separate consumer group for each pipeline; each group tracks its own offset independently
With three consumer groups, each pipeline reads the full stream from its own position. The archiving pipeline, aggregation pipeline, and ML pipeline each checkpoint independently. If one falls behind or restarts, it resumes from its own saved offset without disturbing the others.
- B
Create three separate Event Hubs in the same namespace and replicate events between them with Event Hubs Capture
Why wrong: Event Hubs Capture writes events to Azure Storage or Data Lake in Avro format for cold archival — it is not a replication mechanism between Event Hubs. Creating three separate hubs with event replication would add latency, cost, and management overhead unnecessarily.
- C
Use a single consumer group and route events to different pipelines by partition key prefix
Why wrong: A single consumer group allows only one active consumer per partition. If three pipelines share a consumer group, they must coordinate or one consumes events and the others miss them. Consumer groups, not partition key filtering, are the correct mechanism for multiple independent consumers.
- D
Enable Event Hubs Capture for all three pipelines so they read from the captured Avro files in storage instead of the Event Hub directly
Why wrong: Capture is an archival feature. Reading from captured Avro files in storage introduces significant latency and requires the ML and aggregation pipelines to read flat files rather than a streaming endpoint. It defeats the real-time purpose of Event Hubs for those consumers.
Quick Answer
The answer is to create a separate consumer group for each pipeline. This works because each consumer group in Event Hubs maintains its own independent offset and checkpoint, allowing multiple consumers to read the same event stream without interference. By assigning a dedicated consumer group to the archival, real-time aggregation, and ML pipelines, each can process events at its own pace from its own position in the stream, ensuring no consumer’s progress affects another. On the AZ-204 exam, this tests your understanding of Event Hubs consumer groups for multiple consumers, a common scenario for decoupled processing. A frequent trap is assuming a single consumer group with multiple partition receivers suffices, but that would share offsets and cause conflicts. Remember the memory tip: “One pipeline, one group—offsets don’t overlap.”
AZ-204 Practice Question: Event Hubs consumer groups for multiple…
This AZ-204 practice question tests your understanding of connect to and consume azure services and third-party services. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: event Hubs consumer groups. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Three analytics pipelines each need to read every event from the same Azure Event Hub: one pipeline archives events to cold storage, one computes real-time aggregations, and one feeds a machine learning model. How should the developer configure Event Hubs to allow all three to consume independently without interfering with each other?
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
Create a separate consumer group for each pipeline; each group tracks its own offset independently
A is correct because each consumer group in Event Hubs maintains its own independent offset and checkpoint, allowing multiple consumers to read the same event stream without interfering. By creating a separate consumer group for each pipeline (archival, real-time aggregation, ML), each pipeline can process events at its own pace and from its own position in the stream, ensuring no consumer's progress affects another.
Key principle: Event Hubs consumer groups
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a separate consumer group for each pipeline; each group tracks its own offset independently
Why this is correct
With three consumer groups, each pipeline reads the full stream from its own position. The archiving pipeline, aggregation pipeline, and ML pipeline each checkpoint independently. If one falls behind or restarts, it resumes from its own saved offset without disturbing the others.
Related concept
Event Hubs consumer groups
- ✗
Create three separate Event Hubs in the same namespace and replicate events between them with Event Hubs Capture
Why it's wrong here
Event Hubs Capture writes events to Azure Storage or Data Lake in Avro format for cold archival — it is not a replication mechanism between Event Hubs. Creating three separate hubs with event replication would add latency, cost, and management overhead unnecessarily.
- ✗
Use a single consumer group and route events to different pipelines by partition key prefix
Why it's wrong here
A single consumer group allows only one active consumer per partition. If three pipelines share a consumer group, they must coordinate or one consumes events and the others miss them. Consumer groups, not partition key filtering, are the correct mechanism for multiple independent consumers.
- ✗
Enable Event Hubs Capture for all three pipelines so they read from the captured Avro files in storage instead of the Event Hub directly
Why it's wrong here
Capture is an archival feature. Reading from captured Avro files in storage introduces significant latency and requires the ML and aggregation pipelines to read flat files rather than a streaming endpoint. It defeats the real-time purpose of Event Hubs for those consumers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse consumer groups with partitions, thinking that multiple consumers must use different partitions to avoid interference, but partitions are for scaling throughput, not for independent offset tracking—consumer groups are the correct abstraction for independent consumption.
Detailed technical explanation
How to think about this question
Under the hood, each consumer group in Event Hubs is a separate view of the event stream, with its own checkpoint store (typically in Azure Blob Storage) that tracks partition offsets. This allows multiple consumer groups to read the same partition at different speeds—for example, a real-time aggregation consumer might process events within seconds, while an archival consumer might lag hours behind without conflict. In a real-world scenario, a single Event Hub with multiple consumer groups is the standard pattern for fan-out architectures, as it avoids the need for event duplication or complex routing logic.
KKey Concepts to Remember
- Event Hubs consumer groups
- independent consumers
- checkpoint offset
- parallel event processing
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Event Hubs consumer groups
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Connect to and consume Azure services and third-party services — This question tests Connect to and consume Azure services and third-party services — Event Hubs consumer groups.
What is the correct answer to this question?
The correct answer is: Create a separate consumer group for each pipeline; each group tracks its own offset independently — A is correct because each consumer group in Event Hubs maintains its own independent offset and checkpoint, allowing multiple consumers to read the same event stream without interfering. By creating a separate consumer group for each pipeline (archival, real-time aggregation, ML), each pipeline can process events at its own pace and from its own position in the stream, ensuring no consumer's progress affects another.
What should I do if I get this AZ-204 question wrong?
Review event Hubs consumer groups, then practise related AZ-204 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Event Hubs consumer groups
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Last reviewed: Jun 11, 2026
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