AI-102 Question 204
Single answerYou are designing a solution on Microsoft Azure that needs to process real-time data from IoT devices and apply machine learning models to detect anomalies. The solution must ensure high throughput and low latency while maintaining scalability. Which Azure service should you choose to orchestrate the data processing pipeline?
- A
Azure Stream Analytics
- B
Azure Data Factory
- C
Azure Machine Learning
- D
Azure Event Hubs
Show answer and explanation
Correct answer: A
Explanation
Azure Stream Analytics is the best choice for this scenario as it is optimized for real-time data processing and analysis, which is crucial for detecting anomalies in IoT data streams. While other services like Azure Machine Learning and Azure Event Hubs play important roles in data workflows, they do not provide the necessary real-time orchestration and processing capabilities required in this use case.
- A. Correct.
Azure Stream Analytics is specifically designed for real-time data processing and complex event handling, making it ideal for processing and analyzing IoT device data with high throughput and low latency.
- B. Incorrect.
Azure Data Factory is designed for batch data processing and ETL workflows, which are not suitable for real-time data processing requirements in this scenario.
- C. Incorrect.
Azure Machine Learning is used for building and deploying machine learning models but does not handle real-time data processing pipelines directly.
- D. Incorrect.
Azure Event Hubs is a data ingestion service that can collect high volumes of data but does not perform real-time data analysis or anomaly detection.