You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

• A. Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
• B. Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
• C. Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
• D. Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
• E. Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.

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You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

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

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You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

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

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You are building a model training pipeline in Snowflake using Snowpark Python. You want to leverage a pre-trained model from Hugging Face Transformers for a text classification task, fine-tuning it with your own labeled data stored in a Snowflake table named 'training_data'. You've chosen the 'transformers' library and plan to use a 'transformers.pipeline' for inference. Which of the following code snippets, when integrated into your Snowpark Python application, will correctly download the pre trained model and tokenizer, prepare the data, perform fine-tuning, and then save the fine-tuned model to a Snowflake stage?

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

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A data scientist is tasked with identifying customer segments for a new marketing campaign using transaction data stored in Snowflake. The transaction data includes features like transaction amount, frequency, recency, and product category. Which unsupervised learning algorithm would be MOST appropriate for this task, considering scalability and Snowflake's data processing capabilities, and what preprocessing steps are crucial before applying the algorithm?

• A. Principal Component Analysis (PCA) followed by K-Means. This reduces dimensionality and then clusters, improving the visualization of the cluster.
• B. K-Means clustering, after applying min-max scaling to numerical features and converting categorical features to numerical representation. The optimal 'k' (number of clusters) should be determined using the elbow method or silhouette analysis.
• C. K-Means clustering, after standardizing numerical features (transaction amount, frequency, recency) and using one-hot encoding for product category. This is highly scalable within Snowflake using UDFs and SQL.
• D. DBSCAN, using raw data without any scaling or encoding. The algorithm's density-based nature will automatically handle the varying scales of the features.
• E. Hierarchical clustering, using the complete linkage method and Euclidean distance. No preprocessing is necessary, as hierarchical clustering can handle raw data.

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