Microsoft Fabric Analytics Engineer Associate DP-600
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As a Microsoft Fabric Analytics Engineer Associate working on a data analytics project for a financial services company, your goal is to detect and prevent fraudulent transactions. The company has provided you with a large dataset containing transaction records, including transaction amounts, dates, and customer information. Given the company's requirements for high accuracy, scalability, and compliance with financial regulations, which of the following techniques would be the MOST comprehensive approach to analyze this dataset and identify potential fraudulent transactions? Choose the best option.
Explanation:
The most comprehensive approach to analyzing the dataset for fraudulent transactions involves combining anomaly detection, supervised learning, and association rule mining techniques. Anomaly detection helps in identifying transactions that deviate from normal patterns, supervised learning allows for the classification of transactions based on historical data, and association rule mining uncovers relationships between transaction features and fraudulent activity. This multi-faceted approach ensures high accuracy, scalability, and compliance with financial regulations by covering all aspects of fraud detection.