Explanation:
Option B is the correct answer. Using Azure Stream Analytics to process the data in real-time as it is generated by the IoT devices ensures efficient and timely data processing. Leveraging Azure Event Hubs for data ingestion allows you to handle high-throughput and scalable data streams. Azure Functions can be used for additional processing tasks, providing a serverless compute option for event-driven scenarios.
Ultimate access to all questions.
Your company is using Azure AI services to analyze and process data from IoT devices. The data generated by these devices is time-sensitive and requires real-time processing. What strategies should you implement to ensure efficient and timely data processing?
A
Store the data in Azure Blob Storage and process it in batches at regular intervals.
B
Use Azure Stream Analytics to process the data in real-time as it is generated by the IoT devices, leveraging Azure Event Hubs for data ingestion and Azure Functions for additional processing.
C
Disable real-time processing and rely on manual data processing by the AI service.
D
Use Azure Data Lake Storage to store the data and process it using Azure Databricks, ignoring the need for real-time processing.
No comments yet.