The answer is Azure Stream Analytics, because it is the only Azure service that natively reads from Event Hubs and writes to ADLS Gen2 with built-in support for the exact folder structure /raw/{eventhub}/{yyyy}/{MM}/{dd}/{HH}/{mm} using its time-based partitioning feature. This service processes data in near real-time with sub-minute latency and outputs directly in Avro format, satisfying all requirements without custom code or additional orchestration. On the DP-203 exam, this scenario tests your understanding of Azure Stream Analytics’ native output partitioning to ADLS Gen2, often appearing as a distractor against Azure Data Factory or Databricks—both of which lack native Event Hubs ingestion with this precise folder pattern. A common trap is choosing Azure Data Factory, which requires additional triggers and cannot achieve sub-minute latency for streaming. Memory tip: think “Stream Analytics = Stream to Structure” — it directly maps Event Hubs streams to the hierarchical folder path you define in the output sink.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
You need to store streaming data from Azure Event Hubs into Azure Data Lake Storage Gen2 in near real-time. The data should be stored in Avro format with a folder structure: /raw/{eventhub}/{yyyy}/{MM}/{dd}/{HH}/{mm}. Which Azure service should you use to ingest the data?
Event Hubs Capture feature to automatically capture events to ADLS Gen2.
Why wrong: Capture uses a predefined path pattern (e.g., {Namespace}/{EventHub}/{PartitionId}/{Year}/{Month}/{Day}/{Hour}/{Minute}), not the exact required pattern.
B
Azure Stream Analytics with a job that reads from Event Hubs and writes to ADLS Gen2.
Stream Analytics supports Avro output and custom partition path patterns.
C
Azure Data Factory with a tumbling window trigger to copy data from Event Hubs every 5 minutes.
Why wrong: ADF is not designed for near real-time streaming ingestion.
D
Azure Databricks with Auto Loader to read from Event Hubs and write to ADLS Gen2.
Why wrong: Auto Loader is for file ingestion, not for Event Hubs streaming.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Stream Analytics with a job that reads from Event Hubs and writes to ADLS Gen2.
Azure Stream Analytics is the correct choice because it natively supports reading from Event Hubs and writing to ADLS Gen2 with built-in time-based partitioning into the exact folder structure /raw/{eventhub}/{yyyy}/{MM}/{dd}/{HH}/{mm}. It provides near real-time processing with sub-minute latency and can output data in Avro format directly, meeting all requirements without additional code or orchestration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Event Hubs Capture feature to automatically capture events to ADLS Gen2.
Why it's wrong here
Capture uses a predefined path pattern (e.g., {Namespace}/{EventHub}/{PartitionId}/{Year}/{Month}/{Day}/{Hour}/{Minute}), not the exact required pattern.
✓
Azure Stream Analytics with a job that reads from Event Hubs and writes to ADLS Gen2.
Why this is correct
Stream Analytics supports Avro output and custom partition path patterns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory with a tumbling window trigger to copy data from Event Hubs every 5 minutes.
Why it's wrong here
ADF is not designed for near real-time streaming ingestion.
✗
Azure Databricks with Auto Loader to read from Event Hubs and write to ADLS Gen2.
Why it's wrong here
Auto Loader is for file ingestion, not for Event Hubs streaming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Event Hubs Capture because it seems like a simple 'capture to storage' feature, but they overlook the requirement for near real-time per-minute partitioning, which Capture cannot achieve due to its fixed 5-minute minimum window.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a temporal windowing mechanism (e.g., TumblingWindow) to batch events into output files, and its output to ADLS Gen2 supports custom path patterns with {date} and {time} tokens that map to the required folder hierarchy. Under the hood, Stream Analytics leverages checkpointing and exactly-once semantics for Event Hubs, ensuring no data loss even during failures, and the Avro format is natively supported without custom serializers.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Stream Analytics with a job that reads from Event Hubs and writes to ADLS Gen2. — Azure Stream Analytics is the correct choice because it natively supports reading from Event Hubs and writing to ADLS Gen2 with built-in time-based partitioning into the exact folder structure /raw/{eventhub}/{yyyy}/{MM}/{dd}/{HH}/{mm}. It provides near real-time processing with sub-minute latency and can output data in Avro format directly, meeting all requirements without additional code or orchestration.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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