The answer is Azure Event Hubs and Azure Stream Analytics. Azure Event Hubs is the correct ingestion service because it is a fully managed, high-throughput data streaming platform capable of capturing up to 100 MB/s of sensor data from manufacturing machines, while Azure Stream Analytics provides the real-time processing engine that can query that streaming data using SQL-like syntax and output results directly to a dashboard for live monitoring. On the DP-203 exam, this pairing tests your understanding of the end-to-end real-time streaming data pipeline: Event Hubs handles the ingestion layer, and Stream Analytics handles the transformation and output layer. A common trap is selecting Azure Data Lake Storage Gen2 as the processing service, but that is a storage sink, not a real-time analytics engine. Memory tip: think of Event Hubs as the “door” for streaming data and Stream Analytics as the “brain” that turns it into dashboard insights.
DP-203 Design and implement data storage Practice Question
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 manufacturing company that collects sensor data from machines. The data is stored in Azure Data Lake Storage Gen2. You need to ensure that the solution can handle large volumes of streaming data (up to 100 MB/s) and provide real-time dashboards. Which TWO services should you include?
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 is a real-time analytics service designed to process high-velocity streaming data (up to 100 MB/s) from sources like Event Hubs and output to dashboards and storage. It provides low-latency, SQL-based querying for real-time dashboards, making it ideal for manufacturing sensor data scenarios.
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 Analysis Services
Why it's wrong here
Analysis Services is for multidimensional models, not real-time streaming.
✗
Azure Data Factory
Why it's wrong here
Data Factory is for batch data integration, not real-time streaming.
✗
Azure Databricks
Why it's wrong here
Databricks can handle streaming but is overkill for simple real-time dashboards and may introduce latency.
✓
Azure Stream Analytics
Why this is correct
Stream Analytics processes streaming data and can output to real-time dashboards.
Related concept
Read the scenario before looking for a memorised answer.
✓
Azure Event Hubs
Why this is correct
Event Hubs can ingest high-throughput streaming data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that Azure Databricks is a real-time dashboard service, but it is primarily a processing engine that requires additional integration for dashboard output, whereas Stream Analytics is purpose-built for direct, low-latency dashboarding.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a temporal SQL engine that can handle up to 1 GB/s throughput with sub-second latency when paired with Event Hubs, leveraging partitioning and watermarking for exactly-once semantics. In real-world manufacturing, this combination allows for real-time anomaly detection on sensor data, such as vibration or temperature spikes, with output to Power BI for live dashboards. Event Hubs provides a partitioned, durable event ingestion layer that can scale to millions of events per second, ensuring no data loss during bursts.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Stream Analytics — Azure Stream Analytics is correct because it is a real-time analytics service designed to process high-velocity streaming data (up to 100 MB/s) from sources like Event Hubs and output to dashboards and storage. It provides low-latency, SQL-based querying for real-time dashboards, making it ideal for manufacturing sensor data scenarios.
What should I do if I get this DP-203 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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Question Discussion
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