The correct approach is to ingest data into Azure Event Hubs and use Azure Stream Analytics to output to Power BI for real-time dashboards. This solution directly addresses the business requirement for near real-time analytics azure by replacing the batch-oriented pipeline—currently causing a 30-minute delay—with a streaming architecture that achieves sub-minute latency. Azure Event Hubs provides low-latency, high-throughput ingestion for the clickstream data, while Azure Stream Analytics performs continuous, in-memory processing to transform and output results instantly to Power BI, easily meeting the 10-minute threshold. On the DP-900 exam, this scenario tests your understanding of the difference between batch processing (Azure Databricks + Synapse) and stream processing (Event Hubs + Stream Analytics), a common trap where candidates mistakenly choose a faster batch solution instead of recognizing the need for true streaming. A helpful memory tip is to think of Event Hubs as the “firehose” for real-time data and Stream Analytics as the “instant filter,” enabling dashboards that refresh in seconds, not minutes.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 a data architect at a global retail company. The company has an Azure Data Lake Storage Gen2 account that stores petabytes of clickstream data. They need to provide near real-time analytics dashboards for regional managers. The data arrives in batches every 5 minutes. Currently, they use Azure Databricks to transform the data and load it into Azure Synapse Analytics, but the dashboards show data that is 30 minutes old. The business requires dashboards to reflect data within 10 minutes of ingestion. You propose a new solution. Which approach should you recommend?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ingest data into Azure Event Hubs, use Azure Stream Analytics to process and output to Power BI for real-time dashboards.
Option C is correct because it uses Azure Event Hubs for low-latency ingestion and Azure Stream Analytics for real-time processing, enabling near real-time dashboards in Power BI with sub-minute latency. This architecture bypasses the batch-oriented pipeline that causes the current 30-minute delay, meeting the 10-minute requirement.
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.
✗
Keep current pipeline but replace Synapse with Azure Analysis Services for faster query performance.
Why it's wrong here
Does not address the batch delay.
✗
Use Azure Data Factory with tumbling window triggers every 5 minutes to load data from Data Lake to Synapse.
Why it's wrong here
Still batch processing; may not meet 10-minute SLA.
✓
Ingest data into Azure Event Hubs, use Azure Stream Analytics to process and output to Power BI for real-time dashboards.
Why this is correct
Stream Analytics provides low-latency streaming to Power BI.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of Databricks clusters and use Auto Loader to speed up transformations.
Why it's wrong here
May reduce processing time but still batch-oriented.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume batch tools like Data Factory or Databricks can be tuned to meet near real-time SLAs, but they fundamentally operate on file-based or micro-batch paradigms that cannot match the sub-minute latency of a true streaming pipeline with Event Hubs and Stream Analytics.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language to process data in-memory with sub-second latency, leveraging temporal windows (e.g., tumbling, hopping) for aggregations. Event Hubs supports up to 1 MB/s per throughput unit and can ingest millions of events per second, making it suitable for high-volume clickstream data. In practice, this architecture can achieve end-to-end latency of under 5 seconds from ingestion to dashboard refresh, far exceeding the 10-minute requirement.
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-900 question in full detail.
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: Ingest data into Azure Event Hubs, use Azure Stream Analytics to process and output to Power BI for real-time dashboards. — Option C is correct because it uses Azure Event Hubs for low-latency ingestion and Azure Stream Analytics for real-time processing, enabling near real-time dashboards in Power BI with sub-minute latency. This architecture bypasses the batch-oriented pipeline that causes the current 30-minute delay, meeting the 10-minute requirement.
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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Question Discussion
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