Quick Answer
The answer is Azure Stream Analytics and Power BI. This combination is correct because Azure Stream Analytics processes streaming data in real time from sources like Event Hubs or IoT Hub, while Power BI ingests that processed output to create live dashboards with push datasets and real-time tiles that update automatically with low latency. On the Microsoft Azure Data Fundamentals DP-900 exam, this pairing tests your understanding of which services handle real-time ingestion versus visualization—a common trap is selecting only one service or confusing Azure Stream Analytics with Azure Data Factory, which is batch-oriented. Remember that Stream Analytics does the heavy lifting of continuous querying, and Power BI provides the instant visual feedback. A helpful memory tip: think of Stream Analytics as the engine that keeps data moving, and Power BI as the dashboard that never sleeps—together they deliver true real-time analytics without batch delays.
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.
Which TWO Azure services can be used to build a real-time analytics solution that ingests streaming data and provides dashboards with low latency? (Choose two.)
Power BI
Provides real-time dashboards via DirectQuery or streaming datasets.
Azure Data Factory
Why wrong: Orchestration service, not real-time stream processing.
Azure Data Lake Storage Gen2
Why wrong: Storage service, not real-time processing.
Azure HDInsight with Spark
Why wrong: Primarily batch processing; not optimized for low-latency dashboards.
Azure Stream Analytics
Processes streaming data in real time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Power BI
Power BI is correct because it can connect to real-time data sources like Azure Stream Analytics or Event Hubs to create streaming datasets and dashboards that update automatically with low latency. It supports push datasets and real-time tiles, enabling live visualization of streaming data without batch processing delays.
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.
Power BI
Why this is correct
Provides real-time dashboards via DirectQuery or streaming datasets.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Factory
Why it's wrong here
Orchestration service, not real-time stream processing.
Azure Data Lake Storage Gen2
Why it's wrong here
Storage service, not real-time processing.
Azure HDInsight with Spark
Why it's wrong here
Primarily batch processing; not optimized for low-latency dashboards.
Azure Stream Analytics
Why this is correct
Processes streaming data in real time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse batch-oriented services like Azure Data Factory or storage-only services like Data Lake Storage Gen2 with real-time analytics capabilities, or they assume HDInsight with Spark alone provides built-in dashboards, when in fact it requires a separate visualization layer.
Detailed technical explanation
Azure Stream Analytics uses a SQL-like query language to process streaming data from sources like Event Hubs or IoT Hub, and can output directly to Power BI via the Power BI output adapter, enabling sub-second latency dashboards. Power BI's real-time streaming datasets use the REST API or Azure Stream Analytics to push data into a memory-optimized model, which refreshes tiles automatically without requiring a scheduled refresh. In a real-world scenario, a manufacturing company could use Stream Analytics to filter sensor data from IoT devices and push alerts and metrics to a Power BI dashboard for live monitoring of production line efficiency.
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.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Power BI — Power BI is correct because it can connect to real-time data sources like Azure Stream Analytics or Event Hubs to create streaming datasets and dashboards that update automatically with low latency. It supports push datasets and real-time tiles, enabling live visualization of streaming data without batch processing delays.
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
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.
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