Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Identify the technology used to analyze data for patterns of fraud.

1. Natural language processing

2. Anomaly detection

3. Machine learning (Regression)

4. Computer vision

The correct answer is: Anomaly detection

Anomaly detection is specifically designed to identify patterns that deviate from the norm within data sets, making it highly effective for detecting fraud. In fraud analysis, standard behavior is established based on historical data, and any unusual activity that deviates from this baseline can be flagged for further investigation. This approach allows organizations to uncover potentially fraudulent transactions by recognizing outliers in the data. While natural language processing can analyze text data and extract meaningful insights from unstructured information, it is not typically used for detecting patterns in numerical or transactional data related to fraud. Machine learning, particularly regression, focuses on predicting continuous outcomes rather than identifying anomalous behavior. Although regression analysis can be applied in various contexts, it does not directly address the identification of fraud patterns. Computer vision pertains to processing and interpreting visual data from the world, such as images and videos. While it is useful in some fraud detection scenarios, particularly in visual inspections or surveillance, it does not encompass the broader scope of analyzing data sets for fraud patterns as effectively as anomaly detection. Thus, anomaly detection is the most appropriate technology for analyzing data for patterns of fraud.