- A
Azure Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics
Correct. Event Hubs ingests the streaming data, Stream Analytics performs real-time transformations and aggregations, and Synapse Analytics stores the results for historical analysis and BI reporting.
- B
Azure IoT Hub -> Azure Data Factory -> Azure Cosmos DB
Why wrong: Incorrect. IoT Hub can ingest data, but Azure Data Factory is a batch orchestration service, not a real-time stream processor. Cosmos DB is a NoSQL database, not optimized for large-scale analytics queries like Synapse.
- C
Azure Blob Storage -> Azure Databricks -> Azure SQL Database
Why wrong: Incorrect. Blob Storage is for batch files, not real-time streaming. Databricks can do streaming but requires more setup and is not as seamless as Stream Analytics for simple aggregations. Azure SQL Database is not designed for petabyte-scale analytics.
- D
Azure Service Bus -> Azure Functions -> Azure Table Storage
Why wrong: Incorrect. Service Bus is a message broker for decoupling applications, not optimized for high-throughput event ingestion. Azure Functions can process messages but are not ideal for stateful streaming aggregations. Azure Table Storage is a NoSQL key-value store, not suitable for analytical queries.
Quick Answer
The answer is Azure Event Hubs, Azure Stream Analytics, and Azure Synapse Analytics, in that order. This combination is correct because it forms a complete IoT streaming pipeline where Event Hubs reliably ingests high-throughput sensor data from thousands of devices, Stream Analytics performs real-time aggregations like average temperature per device per minute using its SQL-like query language, and Synapse Analytics stores the results for historical reporting and dashboards. On the DP-900 exam, this scenario tests your understanding of how Azure services handle real-time data processing versus batch processing; a common trap is choosing Azure Data Lake Storage or Azure SQL Database instead of Synapse for the storage layer, but Synapse is specifically designed for large-scale analytics and integrates natively with Stream Analytics. Remember the flow as “Ingest, Analyze, Store”—Event Hubs for ingestion, Stream Analytics for analysis, Synapse for storage—and think of it as a conveyor belt moving data from devices to insights.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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.
A manufacturing company needs to build an analytics solution for IoT sensor data. Thousands of devices send real-time temperature and vibration readings. The solution must: (1) ingest the streaming data reliably, (2) perform real-time aggregations (e.g., average temperature per device every minute), and (3) store the aggregated results in Azure Synapse Analytics for historical reporting and dashboards. Which combination of Azure services should be used?
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
Azure Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics
Azure Event Hubs is designed for high-throughput, reliable ingestion of streaming data from millions of IoT devices. Azure Stream Analytics can then perform real-time aggregations (like average temperature per device per minute) using a SQL-like query language. Finally, Azure Synapse Analytics provides a dedicated SQL pool or serverless SQL endpoint for storing and querying the aggregated results, enabling historical reporting and dashboards.
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.
- ✓
Azure Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics
Why this is correct
Correct. Event Hubs ingests the streaming data, Stream Analytics performs real-time transformations and aggregations, and Synapse Analytics stores the results for historical analysis and BI reporting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure IoT Hub -> Azure Data Factory -> Azure Cosmos DB
Why it's wrong here
Incorrect. IoT Hub can ingest data, but Azure Data Factory is a batch orchestration service, not a real-time stream processor. Cosmos DB is a NoSQL database, not optimized for large-scale analytics queries like Synapse.
- ✗
Azure Blob Storage -> Azure Databricks -> Azure SQL Database
Why it's wrong here
Incorrect. Blob Storage is for batch files, not real-time streaming. Databricks can do streaming but requires more setup and is not as seamless as Stream Analytics for simple aggregations. Azure SQL Database is not designed for petabyte-scale analytics.
- ✗
Azure Service Bus -> Azure Functions -> Azure Table Storage
Why it's wrong here
Incorrect. Service Bus is a message broker for decoupling applications, not optimized for high-throughput event ingestion. Azure Functions can process messages but are not ideal for stateful streaming aggregations. Azure Table Storage is a NoSQL key-value store, not suitable for analytical queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure IoT Hub with Azure Event Hubs, thinking IoT Hub is required for all IoT scenarios, but Event Hubs is the correct choice for pure telemetry ingestion without device management needs.
Detailed technical explanation
How to think about this question
Azure Event Hubs uses an AMQP 1.0 protocol for high-throughput ingestion, supporting up to 1 MB/s per throughput unit, and can be configured with a capture feature to automatically land raw data into Blob Storage for replay. Azure Stream Analytics uses a temporal windowing model (tumbling, hopping, sliding) to perform aggregations in-memory with exactly-once semantics when writing to Azure Synapse Analytics via the dedicated SQL pool connector. In a real-world scenario, if the average temperature per device per minute is calculated, Stream Analytics can output to a Synapse table partitioned by device ID and timestamp for efficient querying.
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics — Azure Event Hubs is designed for high-throughput, reliable ingestion of streaming data from millions of IoT devices. Azure Stream Analytics can then perform real-time aggregations (like average temperature per device per minute) using a SQL-like query language. Finally, Azure Synapse Analytics provides a dedicated SQL pool or serverless SQL endpoint for storing and querying the aggregated results, enabling historical reporting and dashboards.
What should I do if I get this DP-900 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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Last reviewed: Jun 11, 2026
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