- A
Use Azure Databricks with Structured Streaming, store data in Delta Lake, and use Power BI for real-time dashboards
Why wrong: Databricks Structured Streaming can handle the load but introduces higher complexity and latency compared to Stream Analytics for simple filtering and alerting.
- B
Use Azure Data Factory to batch ingest events every minute, store in Azure Blob Storage, and use Azure Analysis Services for historical analytics
Why wrong: Batch processing with Data Factory cannot meet the 10-second alert requirement.
- C
Use Azure Functions to process events, store data in Azure Cosmos DB, and use Power BI for historical analytics
Why wrong: Azure Functions may not scale to 10,000 events/sec with 10-second latency; Cosmos DB is expensive for raw storage.
- 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
Stream Analytics provides low-latency stream processing, Data Lake Storage is cost-effective for raw data, and Synapse Serverless allows querying the data lake.
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 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.
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 for real-time processing and alerting, output data to Azure Data Lake Storage, and use Azure Synapse Serverless SQL for historical analytics
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.
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 Databricks with Structured Streaming, store data in Delta Lake, and use Power BI for real-time dashboards
Why it's wrong here
Databricks Structured Streaming can handle the load but introduces higher complexity and latency compared to Stream Analytics for simple filtering and alerting.
- ✗
Use Azure Data Factory to batch ingest events every minute, store in Azure Blob Storage, and use Azure Analysis Services for historical analytics
Why it's wrong here
Batch processing with Data Factory cannot meet the 10-second alert requirement.
- ✗
Use Azure Functions to process events, store data in Azure Cosmos DB, and use Power BI for historical analytics
Why it's wrong here
Azure Functions may not scale to 10,000 events/sec with 10-second latency; Cosmos DB is expensive for raw storage.
- ✓
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 this is correct
Stream Analytics provides low-latency stream processing, Data Lake Storage is cost-effective for raw data, and Synapse Serverless allows querying the data lake.
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 confuse real-time processing with batch or micro-batch tools (like Databricks or Data Factory) or choose a transactional database (Cosmos DB) for raw storage, overlooking the cost and latency trade-offs required for high-throughput IoT scenarios.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language with temporal windows (e.g., TumblingWindow, HoppingWindow) to compute alerts in near real-time, leveraging checkpointing and exactly-once semantics. ADLS Gen2 provides hierarchical namespace and POSIX-like ACLs, enabling cost-effective storage at $0.018/GB/month for cool tier, while Azure Synapse Serverless SQL uses a pay-per-query model with T-SQL over Parquet/CSV files, eliminating the need for a dedicated SQL pool for historical analysis.
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 for real-time processing and alerting, output data to Azure Data Lake Storage, and use Azure Synapse Serverless SQL for historical analytics — 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.
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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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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