DP-900 Describe an analytics workload on Azure Practice Question
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.
⚠ Common exam trap
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.
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
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.
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
Azure Databricks with Structured Streaming is built on Apache Spark, which can indeed process high-volume event streams, but it introduces significant operational overhead: you must manage clusters, configure streaming checkpoints, and tune Spark jobs. For a simple alerting rule on 10,000 events/sec, Azure Stream Analytics offers a fully managed, declarative SQL-based engine with sub-second latency, whereas Databricks typically incurs seconds of latency from Spark job scheduling and micro-batch intervals. Delta Lake is excellent for ACID transactions and time travel, but landing raw streaming data directly into Azure Data Lake Storage is cheaper and simpler, and Power BI real-time dashboards cannot consume Delta Lake natively without additional services like Stream Analytics as a bridge.
- ✗
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
Azure Data Factory is an orchestration and batch ETL tool, not a real-time stream processor. A one-minute batch interval means events can sit in the source for up to 60 seconds before ingestion, which immediately violates the 10-second alert requirement. Even if you shrank the interval, the scheduling overhead and lack of native event processing make it impractical for low-latency alerts. Azure Blob Storage is fine for raw data, but Azure Analysis Services is a semantic modeling layer designed for curated, aggregated data, not for directly querying raw files in a data lake; Azure Synapse Serverless SQL would be a far better fit for historical analytics over Blob Storage.
- ✗
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 is event-driven and can scale horizontally, but at 10,000 events/sec, each invocation adds per-execution overhead, including cold starts and runtime initialization, making it risky to guarantee 10-second end-to-end latency for alerting. Storing all raw event data in Azure Cosmos DB is cost-prohibitive because Cosmos DB charges per request unit (RU) and is optimized for operational workloads, not high-velocity telemetry storage. Additionally, using Power BI to query Cosmos DB for historical analytics is inefficient because Power BI's connector is designed for small-to-medium result sets; the correct pattern is to land raw data in Data Lake Storage and use a serverless SQL engine to handle large-scale analytics without provisioning infrastructure.
- ✓
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
Azure Stream Analytics is a fully managed stream processing engine that uses SQL-like queries to filter, aggregate, and emit alerts in real time, easily handling 10,000 events/sec with partitioning and low latency. It can write raw or processed output to Azure Data Lake Storage, which provides cost-effective, scalable storage for high-volume telemetry. Azure Synapse Serverless SQL can then query those files directly using T-SQL, enabling historical analytics on the same data lake without loading it into a separate store or managing compute. This combination cleanly separates the hot path (real-time alerting) from the cold path (historical analytics) while minimizing operational overhead and meeting all latency requirements.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
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Data Roles and Core Concepts
Key term
Trigger
In Azure data services, a trigger is a predefined automatic action that initiates a process when a specific event occurs, such as data arriving or a schedule being met.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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