You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:

You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?

• A. Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.
• B. Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.
• C. Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.
• D. Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
• E. Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'

You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

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

You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

• A. Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.
• B. Calculate the average purchase frequency across all customers using and group by 'customer_id'.
• C. Use the window function to divide customers into quantiles based on their total purchase count.
• D. Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
• E. Create a UDF that computes the purchase frequency for a single user and apply it to all customers.

A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

• A. While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
• B. Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
• C. Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
• D. Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).
• E. Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.

You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

• A. Accuracy and ROC AUC.
• B. Precision and Fl-score.
• C. Accuracy and Recall.
• D. ROC AUC and Recall.
• E. Precision, Recall, and Fl-score.