A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

• A. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
• B. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
• C. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
• D. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.
• E. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.

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You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

• A. Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.
• B. Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.
• C. Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.
• D. Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.
• E. Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.

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You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

• A. Power Transformer (Yeo-Johnson or Box-Cox)
• B. MinMaxScaler (Min-Max scaling)
• C. StandardScaler (Z-score normalization)
• D. No scaling is needed as tree-based models are robust to skewed data.
• E. RobustScaler (using interquartile range)

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You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

• A. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
• B. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
• C. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.
  
• D. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
• E. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.
  

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A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

• A. Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.
• B. Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
• C. Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.
• D. Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
• E. Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.

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