DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company needs to build an analytics solution for IoT sensor data. Thousands of devices send real-time temperature and vibration readings. The solution must: (1) ingest the streaming data reliably, (2) perform real-time aggregations (e.g., average temperature per device every minute), and (3) store the aggregated results in Azure Synapse Analytics for historical reporting and dashboards. Which combination of Azure services should be used?
⚠ Common exam trap
Candidates often confuse Azure IoT Hub with Azure Event Hubs, thinking IoT Hub is required for all IoT scenarios, but Event Hubs is the correct choice for pure telemetry ingestion without device management needs.
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
✓
Azure Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics
Azure Event Hubs is designed for high-throughput, reliable ingestion of streaming data from millions of IoT devices. Azure Stream Analytics can then perform real-time aggregations (like average temperature per device per minute) using a SQL-like query language. Finally, Azure Synapse Analytics provides a dedicated SQL pool or serverless SQL endpoint for storing and querying the aggregated results, enabling historical reporting and dashboards.
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 Event Hubs -> Azure Stream Analytics -> Azure Synapse Analytics
Why this is correct
This is the correct architecture because each service is optimized for its stage in a real-time analytics pipeline. Azure Event Hubs is a fully managed, multi-tenant event ingestion platform that can accept millions of events per second from numerous producers, with built-in partitioning and retention to buffer streaming data. Azure Stream Analytics then consumes that data in real time, using a SQL-like language to perform continuous, stateful operations such as tumbling and hopping windows, aggregations, and joins with reference data. Finally, Azure Synapse Analytics provides a dedicated SQL pool with massively parallel processing (MPP) and columnstore indexes, making it ideal for high-performance, petabyte-scale historical analysis and BI reporting on the processed results.
- ✗
Azure IoT Hub -> Azure Data Factory -> Azure Cosmos DB
Why it's wrong here
This pipeline is incorrect for real-time analytics because Azure IoT Hub is designed for IoT device connectivity and management—not as a general-purpose, high-throughput event broker; it also emits device telemetry, but its primary role is device identity and bidirectional command/control. Azure Data Factory is a batch-oriented orchestration and data movement service (ETL/ELT) that lacks native support for continuous stream processing, windowing, or event-time aggregation, so it cannot perform the transformations required in a low-latency analytics solution. Azure Cosmos DB is a globally distributed, multi-model NoSQL database optimized for single-digit-millisecond point reads/writes and transactional consistency, not for complex analytical queries or large-scale columnar scans; it would require expensive cross-partition queries and still lack the performance of a columnar data warehouse. To make this work, you would need to add a stream processor like Stream Analytics, and replace Cosmos DB with an analytical store like Synapse.
- ✗
Azure Blob Storage -> Azure Databricks -> Azure SQL Database
Why it's wrong here
Incorrect. Blob Storage is for batch files, not real-time streaming. Databricks can do streaming but requires more setup and is not as seamless as Stream Analytics for simple aggregations. Azure SQL Database is not designed for petabyte-scale analytics.
- ✗
Azure Service Bus -> Azure Functions -> Azure Table Storage
Why it's wrong here
Incorrect. Service Bus is a message broker for decoupling applications, not optimized for high-throughput event ingestion. Azure Functions can process messages but are not ideal for stateful streaming aggregations. Azure Table Storage is a NoSQL key-value store, not suitable for analytical queries.
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
Learn chapter
Data Roles and Core Concepts
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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