Exam DSA-C03 Topic 1 Question 251 Discussion

Actual exam question for Snowflake's DSA-C03 exam
Question #: 251
Topic #: 1
A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

Suggested Answer: E Vote an answer

Option E is the MOST robust and efficient. It uses the interquartile range (IQR) method, which is less sensitive to extreme outliers than the z-score method in Option A. It also utilizes 'approx_quantile' and is therefore more optimized for Snowflake large datasets. The median is also a more robust measure of central tendency for imputation than the mean when dealing with outliers. Option C uses a hard-coded threshold for outlier removal and imputes with 0, which is not adaptive or robust. Option D skips data cleaning altogether.Option A uses z-score which may work however, since IoT has continuous streaming data quantile based outlier removal is better. It is more optimised for large dataset and better at handling streaming datasets.

by Kerwin at Jun 07, 2025, 10:42 PM

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