Quick Answer
The answer is Azure Event Hubs, the correct choice for raw data ingestion for streaming IoT data because it is a fully managed, real-time ingestion service designed to handle millions of events per second while preserving data in its original format within a partitioned, ordered log. This allows immediate processing of the raw stream and, with Event Hubs Capture, enables long-term retention for later transformation and analytics, directly addressing the need to store data before any schema or structure is applied. On the Microsoft Azure Data Engineer Associate DP-203 exam, this question tests your understanding of the distinction between ingestion services—Event Hubs for raw, high-throughput capture versus services like Azure Stream Analytics or Data Lake Storage, which are used after ingestion for processing or storage. A common trap is confusing Event Hubs with IoT Hub, but remember: IoT Hub adds device management and bi-directional communication, while Event Hubs is purely for high-scale, raw event ingestion. Memory tip: think "Event Hubs = Event Highway" for raw, fast, and ordered data flow.
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 are designing a data storage solution for real-time streaming data from IoT devices. The data must be stored in its original format for immediate processing and later transformed for analytics. Which Azure service should you use for raw data ingestion?
Azure Data Lake Storage Gen2
Why wrong: A is wrong because it is a storage service, not an ingestion service.
Azure Event Hubs
C is correct because Event Hubs is designed for real-time data ingestion.
Azure Stream Analytics
Why wrong: D is wrong because it processes data but does not store raw data.
Azure Data Factory
Why wrong: B is wrong because it is for batch/ETL, not real-time streaming.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Event Hubs
Azure Event Hubs is a fully managed, real-time data ingestion service optimized for high-throughput streaming data from IoT devices. It can receive millions of events per second, store them in a partitioned, ordered log for immediate processing, and retain them for up to 7 days (or longer with Event Hubs Capture) for later transformation and analytics. This makes it the correct choice for raw data ingestion before any transformation occurs.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Data Lake Storage Gen2
Why it's wrong here
A is wrong because it is a storage service, not an ingestion service.
Azure Event Hubs
Why this is correct
C is correct because Event Hubs is designed for real-time data ingestion.
Related concept
Read the scenario before looking for a memorised answer.
Azure Stream Analytics
Why it's wrong here
D is wrong because it processes data but does not store raw data.
Azure Data Factory
Why it's wrong here
B is wrong because it is for batch/ETL, not real-time streaming.
Common exam traps
The trap here is that candidates confuse data ingestion (Event Hubs) with data storage (Data Lake Storage) or data processing (Stream Analytics), assuming a single service must handle both raw capture and transformation, when in fact the question explicitly asks for raw data ingestion only.
Detailed technical explanation
Event Hubs uses an AMQP 1.0 and HTTPS-based protocol for ingestion, with each event stored in a partitioned log that guarantees ordering within a partition. The Capture feature automatically writes raw events to Azure Blob Storage or Data Lake Storage in Avro format, enabling seamless later transformation without additional code. In a real-world scenario, a fleet of thousands of sensors sending telemetry every second would overwhelm a batch service, but Event Hubs can handle this with auto-inflate and throughput units (TUs) scaling.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Event Hubs — Azure Event Hubs is a fully managed, real-time data ingestion service optimized for high-throughput streaming data from IoT devices. It can receive millions of events per second, store them in a partitioned, ordered log for immediate processing, and retain them for up to 7 days (or longer with Event Hubs Capture) for later transformation and analytics. This makes it the correct choice for raw data ingestion before any transformation occurs.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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