Exam DSA-C03 Topic 1 Question 182 Discussion

Actual exam question for Snowflake's DSA-C03 exam
Question #: 182
Topic #: 1
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

Suggested Answer: C Vote an answer

Option C is the most effective for this scenario. Focusing on sessions and their durations provides a more meaningful feature for predicting conversion rates. Removing bot traffic (A) might be a useful preprocessing step but doesn't fundamentally address session-level relevance. Option B's logic is flawed removing all Internet Explorer traffic isn't inherently removing irrelevant data. Option D oversimplifies the data, losing valuable information about user behavior within sessions. Option E introduces bias by randomly sampling and removing potentially important patterns, plus it is too simplistic. The code example in C demonstrates how to calculate session duration using Snowpark functions, join the filtered session data back to the original data, and then drop the irrelevant columns.

by Vivien at Sep 22, 2025, 04:38 AM

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