Microsoft Certified: Azure Solutions Architect ExpertDesign identity, governance, and monitoring solutionsHard
A global manufacturing company is deploying a new IoT solution on Azure to monitor factory equipment. They need to collect telemetry data from thousands of devices, process it in real-time, and store it for historical analysis. The solution must also provide insights into equipment health and predict potential failures. Which combination of Azure services should be used for monitoring and data ingestion?
- AAzure Logic Apps, Azure Functions, Azure SQL Database
- BAzure Event Hubs, Azure Stream Analytics, Azure Synapse Analytics
- CAzure IoT Hub, Azure Stream Analytics, Azure Data Lake Storage
- DAzure Service Bus, Azure Data Factory, Azure Cosmos DB
Show answer & explanationAnswer & explanation
Correct answer: C. Azure IoT Hub, Azure Stream Analytics, Azure Data Lake Storage
Azure IoT Hub is specifically designed for secure, bi-directional communication with millions of IoT devices, handling telemetry ingestion. Azure Stream Analytics can process this real-time data for immediate insights, and Azure Data Lake Storage is suitable for cost-effective, large-scale storage of raw and processed data for historical analysis and machine learning.
Why the other options are wrong
- A. Logic Apps and Functions are for orchestration and serverless compute, and SQL Database is not ideal for massive, unstructured IoT data storage and real-time processing.
- B. Event Hubs are good for high-throughput event ingestion but IoT Hub is specialized for device management and communication in IoT scenarios.
- D. Service Bus is for enterprise messaging, Data Factory for ETL, and Cosmos DB for NoSQL data, which might be part of a larger solution but not the primary services for IoT telemetry ingestion, real-time processing, and large-scale historical storage as a core set.
Azure IoT Data Pipeline
A typical Azure IoT data pipeline involves IoT Hub for device ingestion, Stream Analytics for real-time processing, and Data Lake Storage for historical data storage and analysis.
- IoT Hub handles device communication and telemetry.
- Stream Analytics processes data in motion.
- Data Lake Storage provides scalable, cost-effective storage for big data.
Memory trick: IoT Hub: The device's mailbox. Stream Analytics: The real-time sorter. Data Lake: The vast archive.