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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a large dataset of customer transactions in Snowflake. The dataset contains columns like 'customer id' , 'transaction date', 'product category' , and 'transaction_amount'. Your task is to identify fraudulent transactions by detecting anomalies in spending patterns. You decide to use Snowpark for Python to perform time-series aggregation and feature engineering. Given the following Snowpark DataFrame 'transactions_df , which of the following approaches would be MOST efficient for calculating a 7-day rolling average of for each customer, while also handling potential gaps in transaction dates?

A) Use a stored procedure in SQL to iterate over each customer, calculate the rolling average using a cursor and conditional logic for handling missing dates.
B) Use a simple followed by a UDF to calculate the rolling average. Fill in missing dates manually within the UDF.
C) Use 'window.partitionBy('customer_id').orderBy('transaction_date').rowsBetween(-6, Window.currentRow)' within a 'select' statement and handle any missing dates using 'fillna()' after calculating the rolling average.
D) Use a Snowpark Pandas UDF to calculate the rolling average for each customer after collecting all transactions for that customer into a Pandas DataFrame. Handle missing dates using Pandas functionality.
E) Use'window.partitionBy('customer_id').orderBy('transaction_date').rangeBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with a date range table joined to the transactions, filling in missing days before calculating the rolling average with 'transaction_amount' set to 0 for the inserted days.

2. You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

A) Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
B) Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
C) Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.
D) Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.
E) Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.

3. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

4. You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

A) Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
B) Drop one of the independent variable randomly if they seem highly correlated.
C) Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.
D) Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.
E) Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.

5. You're analyzing the performance of two different AIB testing variants of an advertisement. You've collected the following data over a period of one week: Variant A: 1000 impressions, 50 conversions Variant B: 1100 impressions, 66 conversions Which of the following statements are TRUE regarding confidence intervals and statistical significance in this scenario?

A) Increasing the sample size (number of impressions for each variant) will generally widen the confidence interval, making it more likely to contain zero.
B) A narrower confidence interval for the difference in conversion rates implies a higher degree of certainty about the estimated difference.
C) Constructing a 95% confidence interval for the difference in conversion rates between Variant B and Variant A will allow you to assess if there is a statistically significant difference at the 5% significance level. If the confidence interval contains zero, there is no statistically significant difference.
D) Calculating separate confidence intervals for conversion rates A and B, and noting overlap, is an invalid method to infer statistical significance. One must construct confidence interval for the difference in means.
E) If the 95% confidence interval for the conversion rate of Variant A is entirely above the 95% confidence interval for the conversion rate of Variant B, then Variant A is statistically better than Variant B.

Solutions: