Quick Answer
The answer is Azure Stream Analytics and Azure Synapse Analytics. Azure Stream Analytics is correct because it provides real-time stream processing with minimal latency, directly ingesting data from Event Hubs and enabling real-time dashboard queries while simultaneously writing results to a staging store like Azure Data Lake Storage. Azure Synapse Analytics then supports batch analytics by querying that stored historical data, creating a unified solution for both real-time and batch analytics from streaming data. On the DP-203 exam, this scenario tests your understanding of the Lambda architecture pattern, where a speed layer (Stream Analytics) handles live data and a batch layer (Synapse) handles historical analysis. A common trap is choosing Azure Databricks for the real-time path, but Stream Analytics is the native, low-latency service for Event Hubs ingestion. Memory tip: think “Stream for speed, Synapse for deep analysis” to pair the two correctly.
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 a real-time dashboard that displays streaming data from Azure Event Hubs. The data must be stored in a format that supports both real-time and batch analytics with minimal latency. Which TWO technologies should you use?
Azure Stream Analytics
Stream Analytics processes streaming data in real-time.
Azure Data Factory
Why wrong: Primarily for batch data movement, not real-time.
Azure Synapse Analytics
Supports both real-time (via Synapse Link) and batch analytics.
Azure Analysis Services
Why wrong: Used for semantic models, not data storage.
Azure SQL Database
Why wrong: Not optimized for real-time streaming ingestion.
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 Stream Analytics
Azure Stream Analytics is correct because it provides real-time stream processing with low latency, directly ingesting data from Event Hubs and outputting to storage or analytics services. It enables both real-time dashboard queries and batch analytics by writing to a staging store like Azure Data Lake Storage, which can then be queried by Azure Synapse Analytics for historical analysis.
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 Stream Analytics
Why this is correct
Stream Analytics processes streaming data in real-time.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Factory
Why it's wrong here
Primarily for batch data movement, not real-time.
Azure Synapse Analytics
Why this is correct
Supports both real-time (via Synapse Link) and batch analytics.
Related concept
Read the scenario before looking for a memorised answer.
Azure Analysis Services
Why it's wrong here
Used for semantic models, not data storage.
Azure SQL Database
Why it's wrong here
Not optimized for real-time streaming ingestion.
Common exam traps
The trap here is that candidates often confuse Azure Data Factory as a real-time processing tool, but it is strictly a batch orchestration service with no native stream processing capability.
Detailed technical explanation
Under the hood, Azure Stream Analytics uses a SQL-like query language to process data in-memory with sub-second latency, leveraging checkpointing and exactly-once semantics via Event Hubs' partition offsets. For batch analytics, Stream Analytics can write to Azure Data Lake Storage Gen2 in Parquet format, which Azure Synapse Analytics can query using serverless SQL pools or dedicated SQL pools, enabling a lambda architecture pattern. A real-world scenario is a stock trading dashboard where Stream Analytics computes moving averages in real-time while Synapse runs daily risk reports on the same stored data.
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
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 Stream Analytics — Azure Stream Analytics is correct because it provides real-time stream processing with low latency, directly ingesting data from Event Hubs and outputting to storage or analytics services. It enables both real-time dashboard queries and batch analytics by writing to a staging store like Azure Data Lake Storage, which can then be queried by Azure Synapse Analytics for historical analysis.
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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Same concept, more angles
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Variation 1. You need to design a storage solution for streaming data from IoT devices. The solution must support real-time analytics and long-term storage for historical analysis. Which combination of Azure services should you use?
easyWhy D: Azure Event Hubs is designed for high-throughput, low-latency ingestion of streaming data from IoT devices, supporting real-time analytics via integration with Azure Stream Analytics. Azure Data Lake Storage Gen2 provides hierarchical namespace and POSIX-compliant access for long-term storage, enabling efficient historical analysis with tools like Azure Synapse Analytics or Spark. This combination meets both real-time and historical requirements without the limitations of other options.
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Last reviewed: Jun 24, 2026
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