- A
Azure IoT Hub, Azure Data Lake Storage, and Azure Databricks
Why wrong: Data Lake Storage is for batch storage, and Databricks is better for batch processing, not real-time stream processing.
- B
Azure IoT Hub, Azure Stream Analytics, and Azure Data Explorer
IoT Hub ingests device data, Stream Analytics performs real-time transformations, and Data Explorer is a time-series database for fast analytics.
- C
Azure Event Hubs, Azure Functions, and Azure SQL Database
Why wrong: Azure Functions can process streams but SQL Database is not designed for high-ingestion time-series workloads.
- D
Azure Event Hubs, Azure Synapse Pipelines, and Azure Cosmos DB
Why wrong: Synapse Pipelines are for batch orchestration, not real-time, and Cosmos DB is not a time-series database.
Quick Answer
The correct combination is Azure IoT Hub, Azure Stream Analytics, and Azure Data Explorer. Azure IoT Hub serves as the secure ingestion point for streaming sensor data from IoT devices, while Azure Stream Analytics performs real-time transformation and analysis on the data streams as they flow through. Azure Data Explorer then stores the processed output in its optimized time-series database, purpose-built for high-velocity telemetry and log data. On the DP-900 exam, this scenario tests your understanding of how Azure services align with specific pipeline stages: ingestion, processing, and storage. A common trap is confusing Azure SQL Database or Cosmos DB for time-series storage, but remember that Data Explorer is the dedicated service for fast, analytical queries on timestamped data. Memory tip: think “Hub to Stream to Explorer” — each service handles one distinct phase of the real-time IoT pipeline.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. 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 team is designing a data pipeline to process streaming sensor data from IoT devices. The data must be ingested, transformed in real time, and stored in a time-series database. Which combination of Azure services should they use?
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 IoT Hub, Azure Stream Analytics, and Azure Data Explorer
Option B is correct because Azure IoT Hub ingests streaming sensor data from IoT devices, Azure Stream Analytics provides real-time transformation and analysis of the data streams, and Azure Data Explorer (ADX) is a fully managed time-series database optimized for high-velocity telemetry data. This combination directly addresses the requirement for ingestion, real-time transformation, and time-series storage.
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 IoT Hub, Azure Data Lake Storage, and Azure Databricks
Why it's wrong here
Data Lake Storage is for batch storage, and Databricks is better for batch processing, not real-time stream processing.
- ✓
Azure IoT Hub, Azure Stream Analytics, and Azure Data Explorer
Why this is correct
IoT Hub ingests device data, Stream Analytics performs real-time transformations, and Data Explorer is a time-series database for fast analytics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Event Hubs, Azure Functions, and Azure SQL Database
Why it's wrong here
Azure Functions can process streams but SQL Database is not designed for high-ingestion time-series workloads.
- ✗
Azure Event Hubs, Azure Synapse Pipelines, and Azure Cosmos DB
Why it's wrong here
Synapse Pipelines are for batch orchestration, not real-time, and Cosmos DB is not a time-series database.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Explorer with Azure Data Lake Storage or Azure SQL Database, assuming any storage service can handle time-series data, but ADX is the only Azure service purpose-built for high-ingestion-rate time-series analytics with features like materialized views and data sharding.
Detailed technical explanation
How to think about this question
Azure Data Explorer uses a columnar storage engine with built-in indexing and partitioning for time-series data, supporting ingestion from Stream Analytics via the ADX output connector. Stream Analytics uses a SQL-like query language with temporal windows (e.g., Tumbling, Hopping, Sliding) to perform real-time aggregations, while IoT Hub supports MQTT, AMQP, and HTTPS protocols for device connectivity. In a real-world scenario, a manufacturing plant streaming temperature sensor data at 10,000 events per second would use IoT Hub for ingestion, Stream Analytics to calculate moving averages, and ADX to store the raw and aggregated data with automatic hot/cold tiering.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Describe core data concepts — study guide chapter
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure IoT Hub, Azure Stream Analytics, and Azure Data Explorer — Option B is correct because Azure IoT Hub ingests streaming sensor data from IoT devices, Azure Stream Analytics provides real-time transformation and analysis of the data streams, and Azure Data Explorer (ADX) is a fully managed time-series database optimized for high-velocity telemetry data. This combination directly addresses the requirement for ingestion, real-time transformation, and time-series storage.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You need to design a real-time dashboard that displays the number of orders placed in the last hour from an e-commerce application. The application writes orders to Azure Event Hubs. Which Azure service should you use to aggregate the data and serve the dashboard with minimal latency?
medium- A.Azure Databricks Structured Streaming
- ✓ B.Azure Stream Analytics with Power BI output
- C.Azure Analysis Services
- D.Azure Data Factory with tumbling window
Why B: Azure Stream Analytics is purpose-built for real-time data processing from sources like Event Hubs, and its native integration with Power BI enables direct output to a dashboard with sub-second latency. This combination provides the minimal-latency aggregation and serving required for a real-time orders dashboard without additional infrastructure.
Variation 2. A data engineer needs to process streaming data from IoT devices in near real-time and store the results in Azure Cosmos DB. Which Azure service should they use for the stream processing?
easy- A.Azure Synapse Analytics
- B.Azure Databricks
- ✓ C.Azure Stream Analytics
- D.Azure Data Factory
Why C: Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed specifically for low-latency, near-real-time analytics on streaming data. It can ingest data from IoT devices via Event Hubs or IoT Hub, apply SQL-based transformations, and directly output the results to Azure Cosmos DB with millisecond latency, making it ideal for this scenario.
Last reviewed: Jun 24, 2026
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