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Answer: Use Azure Event Hubs to ingest the data and then use Stream Analytics to process the data, handle schema drift, and create windowed aggregates for fraud detection.
Option D is the correct approach as it covers all the necessary components of a stream processing solution for fraud detection. Azure Event Hubs is used to ingest the data, Stream Analytics is used to process the data, handle schema drift, and create windowed aggregates. This approach ensures that the solution can handle real-time data and provide insights into potential fraudulent transactions.
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You are tasked with developing a stream processing solution for a financial services company that needs to monitor and analyze real-time transaction data for fraud detection. The data is being ingested into Azure Event Hubs. How would you approach creating a solution using Stream Analytics and Azure Event Hubs?
A
Use Stream Analytics to create a query that reads data from Azure Event Hubs and writes the output to a storage account.
B
Use Azure Event Hubs to ingest the data and then use Stream Analytics to process the data and create windowed aggregates for fraud detection.
C
Use Azure Event Hubs to ingest the data and then use Stream Analytics to process the data and handle schema drift for fraud detection.
D
Use Azure Event Hubs to ingest the data and then use Stream Analytics to process the data, handle schema drift, and create windowed aggregates for fraud detection.
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