- A
Use Azure Stream Analytics to process the stream and output directly to Azure SQL Database. Use Power BI to query SQL Database for both real-time dashboard and historical analytics.
Why wrong: SQL Database would be very expensive for 5 TB/day and cannot handle the volume efficiently.
- B
Use Azure Event Hubs Capture to store data in Azure Blob Storage, then use Azure Data Factory to transform and load into Azure Synapse Analytics for both dashboard and batch.
Why wrong: Event Hubs Capture introduces a few minutes delay; Data Factory pipelines add further latency, not near-real-time.
- C
Use Azure Databricks with Structured Streaming to process the stream, write to Delta Lake, and use Delta Lake to serve both real-time and batch queries.
Why wrong: Databricks Structured Streaming can handle it but may have higher latency than Stream Analytics for simple dashboard; also more complex to manage.
- D
Use Azure Stream Analytics to process the stream, output to Power BI for real-time dashboard, and simultaneously output raw data to Azure Data Lake Storage. Use Azure Databricks to process the data lake for batch analytics.
Stream Analytics provides low latency for dashboard; Data Lake Storage is cost-effective for large volumes; Databricks handles batch efficiently.
Quick Answer
The correct approach is to use Azure Stream Analytics to process the stream, output to Power BI for the real-time dashboard, and simultaneously output raw data to Azure Data Lake Storage, then use Azure Databricks to process the data lake for daily batch analytics. This design separates the real-time and batch processing paths, which minimizes latency for the dashboard by sending streaming data directly to Power BI without waiting for batch cycles, while maximizing cost efficiency for batch processing by storing raw data cheaply in Azure Data Lake Storage and running compute-intensive analytics only once daily with Databricks. On the DP-900 exam, this scenario tests your understanding of the lambda architecture pattern, where a hot path (Stream Analytics to Power BI) handles low-latency needs and a cold path (Data Lake to Databricks) handles historical analysis. A common trap is trying to use a single tool for both streams, which either increases latency or cost. Remember the memory tip: “Hot path for speed, cold path for savings.”
DP-900 Describe an analytics workload on Azure Practice Question
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.
You are a data architect for a logistics company. The company uses Azure Data Lake Storage Gen2 to store shipment tracking data. The data is ingested from IoT devices on trucks. Each record contains truck ID, timestamp, GPS coordinates, speed, and fuel level. The volume is 5 TB per day. The company wants to build a near-real-time dashboard to monitor truck locations and speeds. They also need to run daily batch analytics to compute fuel efficiency trends. You need to design a solution that minimizes latency for the dashboard and maximizes cost efficiency for batch processing. You plan to use Azure Event Hubs for ingestion. Which approach should you take?
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
Use Azure Stream Analytics to process the stream, output to Power BI for real-time dashboard, and simultaneously output raw data to Azure Data Lake Storage. Use Azure Databricks to process the data lake for batch analytics.
Option D is correct because it separates the real-time and batch processing paths to minimize latency and maximize cost efficiency. Azure Stream Analytics outputs directly to Power BI for near-real-time dashboard updates, while simultaneously writing raw data to Azure Data Lake Storage for cost-effective storage. Azure Databricks then processes the data lake for daily batch analytics, avoiding expensive real-time compute for historical analysis.
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.
- ✗
Use Azure Stream Analytics to process the stream and output directly to Azure SQL Database. Use Power BI to query SQL Database for both real-time dashboard and historical analytics.
Why it's wrong here
SQL Database would be very expensive for 5 TB/day and cannot handle the volume efficiently.
- ✗
Use Azure Event Hubs Capture to store data in Azure Blob Storage, then use Azure Data Factory to transform and load into Azure Synapse Analytics for both dashboard and batch.
Why it's wrong here
Event Hubs Capture introduces a few minutes delay; Data Factory pipelines add further latency, not near-real-time.
- ✗
Use Azure Databricks with Structured Streaming to process the stream, write to Delta Lake, and use Delta Lake to serve both real-time and batch queries.
Why it's wrong here
Databricks Structured Streaming can handle it but may have higher latency than Stream Analytics for simple dashboard; also more complex to manage.
- ✓
Use Azure Stream Analytics to process the stream, output to Power BI for real-time dashboard, and simultaneously output raw data to Azure Data Lake Storage. Use Azure Databricks to process the data lake for batch analytics.
Why this is correct
Stream Analytics provides low latency for dashboard; Data Lake Storage is cost-effective for large volumes; Databricks handles batch efficiently.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a single technology (like Databricks or Synapse) can handle both real-time and batch workloads equally well, but the DP-900 exam tests the understanding that separating the streaming path (Stream Analytics to Power BI) from the batch path (Data Lake to Databricks) optimizes for both latency and cost.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a temporal windowing mechanism (e.g., tumbling, hopping, sliding windows) to process streaming data with sub-second latency, outputting aggregated results to Power BI via the Power BI output adapter. The raw data is simultaneously captured to Azure Data Lake Storage via the Blob Storage/ADLS output adapter, enabling cost-effective storage at ~$0.02/GB/month. Azure Databricks then reads the raw Parquet files from the data lake for batch analytics, leveraging Delta Lake's ACID transactions and optimized file layout for efficient fuel efficiency trend computations.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Azure Stream Analytics to process the stream, output to Power BI for real-time dashboard, and simultaneously output raw data to Azure Data Lake Storage. Use Azure Databricks to process the data lake for batch analytics. — Option D is correct because it separates the real-time and batch processing paths to minimize latency and maximize cost efficiency. Azure Stream Analytics outputs directly to Power BI for near-real-time dashboard updates, while simultaneously writing raw data to Azure Data Lake Storage for cost-effective storage. Azure Databricks then processes the data lake for daily batch analytics, avoiding expensive real-time compute for historical analysis.
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
1 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 are a data architect for a healthcare organization. The organization needs to build a real-time analytics solution to monitor patient vital signs from IoT devices. The data arrives at a rate of 10,000 events per second. Each event contains patient ID, timestamp, heart rate, blood pressure, and oxygen saturation. The solution must alert clinicians within 10 seconds when a patient's vital signs exceed predefined thresholds. Additionally, the solution must store the raw data for historical analysis and compliance. You plan to use Azure Event Hubs for ingestion. Which combination of services should you use to meet the requirements? Consider: processing low latency alerts, storing raw data in cost-effective storage, and enabling historical analytics. You also need to ensure that the solution can scale to handle future growth.
hard- A.Use Azure Databricks with Structured Streaming, store data in Delta Lake, and use Power BI for real-time dashboards
- B.Use Azure Data Factory to batch ingest events every minute, store in Azure Blob Storage, and use Azure Analysis Services for historical analytics
- C.Use Azure Functions to process events, store data in Azure Cosmos DB, and use Power BI for historical analytics
- ✓ D.Use Azure Stream Analytics for real-time processing and alerting, output data to Azure Data Lake Storage, and use Azure Synapse Serverless SQL for historical analytics
Why D: Option D is correct because Azure Stream Analytics provides low-latency (sub-second) stream processing and can trigger alerts within the 10-second requirement. Outputting raw data to Azure Data Lake Storage (ADLS) offers cost-effective storage for compliance, and Azure Synapse Serverless SQL enables on-demand historical analytics without provisioning dedicated compute, scaling automatically for future growth.
Last reviewed: Jun 24, 2026
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