DAA-C01 Exam Dumps | You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table

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Question 76/151

You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table 'WEB EVENTS' with columns 'event_timestamp' (TIMESTAMP NTZ), 'user_id' (VARCHAR), and 'ip_address' (VARCHAR). Which combination of SQL techniques and Snowflake features would be MOST effective in detecting and flagging suspicious bot-like behavior, considering high query performance and scalability?

A. Use a UDF (User-Defined Function) written in Python to perform complex behavioral analysis on user event sequences, checking for patterns like rapid page transitions or form submissions within unrealistic timeframes. Apply this UDF to the 'WEB EVENTS' table.

B. Calculate event frequency per user and IP address using window functions (e.g., 'COUNT() OVER (PARTITION BY user_id, ip_address ORDER BY Then, identify users/lPs with abnormally high event rates within short time intervals using appropriate threshold criteria.

C. Join the table with a publicly available list of known bot IP addresses. Flag any events originating from those IP addresses as potential bot activity. Supplement this with simple frequency counts of events per user.

D. Create a scheduled task that periodically runs a query to analyze the ratio of human-generated events to server-generated events. If the ratio drops below a certain threshold, flag the time period as suspicious.

E. Implement a stored procedure that iterates through each unique IP address in the table, calculating the average time between events for each 12 Flag IP addresses where the average time between events is significantly below a pre-defined threshold.

Correct Answer: B,C

Options B and C offer a good balance of effectiveness and efficiency. Option B uses window functions, a powerful feature within Snowflake for analyzing data within a context (user and IP address). Option C uses a pre-defined list of bots and it is not resource intensive. Option A, while potentially accurate, can be computationally expensive due to the use of a UDF and might affect the overall cluster performance. Option D is better suited to detect DDoS attacks. Option E is inefficient as it iterates through the resultset

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