A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

• A. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
• B. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
• C. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
• D. The CLT is not applicable because the population distribution (call durations) is heavily skewed.
• E. The CLT is applicable, and the sample mean will converge to the population median.

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. 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.
  
• B. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
• C. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
• D. 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.
  
• E. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.

You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

• A. Option B
• B. Option D
• C. Option C
• D. Option A
• E. Option E

You are tasked with identifying fraudulent transactions in a large financial dataset stored in Snowflake using unsupervised learning. The dataset contains features like transaction amount, merchant ID, location, time, and user ID. You decide to use a combination of clustering and anomaly detection techniques. Which of the following steps and techniques would be MOST effective in achieving this goal while leveraging Snowflake's capabilities and minimizing false positives?

• A. Implement an Isolation Forest algorithm directly in SQL using complex JOINs and window functions to identify anomalies based on transaction volume and velocity.
• B. Apply Principal Component Analysis (PCA) for dimensionality reduction, then use DBSCAN clustering to identify dense regions of normal transactions and flag any transaction that is not within a dense region as potentially fraudulent. After, review the anomalous data points.
• C. Use only the 'transaction amount' feature and perform histogram-based anomaly detection in Snowflake SQL by identifying values outside of the common ranges, disregarding other potentially relevant information.
• D. Perform K-means clustering on the entire dataset using all available features, then flag any transaction that falls outside of any cluster as fraudulent. Ignore any feature selection or engineering to simplify the process.
• E. Use a Snowflake Python UDF to perform feature selection, apply a combination of K-means clustering and anomaly detection techniques like Isolation Forest or Local Outlier Factor (LOF), and then score each transaction based on its likelihood of being fraudulent. Tune parameters and use a hold-out validation set to minimize false positives, using a Snowpark DataFrame to retrieve the data.

You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?

• A. Target Encoding (Mean Encoding) using Snowflake SQL, calculating the mean churn rate for each tariff plan and using that as the encoded value. Handle unseen values with the global mean churn rate, being mindful of potential target leakage.
• B. One-Hot Encoding using CREATE OR REPLACE VIEW, handling new values by NULLIF('Unknown', tariff_plan) before encoding, potentially leading to a high number of columns.
• C. Binary Encoding using a UDF to convert each tariff plan into binary code, storing encoded results into snowflake, then splitting the binary representation into separate columns.
• D. Label Encoding using a UDF (User-Defined Function) with a predefined mapping, assigning a new integer to unseen values, and storing the mapping in a separate table in Snowflake.
• E. Hash Encoding (Feature Hashing) using a UDF in Snowflake, with a fixed number of features and a hashing function to map each tariff plan to a feature index, accepting potential collisions. Handle new tariff plans naturally through the hashing function.