DAA-C01 Exam Dumps | You are analyzing website traffic data in Snowflake and want to identify unusual access patterns using

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Snowflake.DAA-C01.v2025-12-12.q151
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Question 10/151

You are analyzing website traffic data in Snowflake and want to identify unusual access patterns using statistical techniques and visualize them. You have a table LOG' with columns (TIMESTAMP_NTZ), 'IP_ADDRESS (VARCHAR), and (VARCHAR). Which of the following approaches, combining Snowflake SQL and visualization techniques, is the MOST effective for detecting and visualizing anomalies in access patterns?

A. Calculate the average number of page accesses per IP address per day using a simple 'AVG' aggregate function and display it on a line chart.

B. Use Snowflake's function to identify the most frequently accessed pages and visualize them in a bar chart, filtering out common pages like the homepage.

C. Calculate the rate of change of access attempts per IP address over short time intervals (e.g., 5 minutes) using 'LAG' or 'LEAD functions, identify IP addresses with significantly high rates of change compared to the historical average (using statistical measures like standard deviation), and visualize these IP addresses and their rate of change on a scatter plot with alerts for outliers.

D. Simply count the total number of accesses per IP address and visualize it in a histogram.

E. Use GROUP BY to find the count of accesses per IP address per page and represent that using a heat map.

Correct Answer: C

Option C is the most effective because it utilizes time series analysis to identify sudden spikes in access attempts, suggesting potential anomalies like bot attacks or brute-force attempts. It incorporates statistical analysis to identify significant deviations from the norm and provides a visual representation for easy identification of outliers. The other options lack the time series and statistical components necessary for robust anomaly detection. Options A, B, D and E, might provide insights into popular pages or IPs, but would not identify anomalous behavior based on rapid changes.

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