You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?

• A. Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
• B. Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
• C. Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
• D. Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
• E. Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.

You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

• A. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
• B. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
• C. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
• D. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
• E. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.

You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

• A. Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).
• B. Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).
• C. Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
• D. Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
• E. Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.

You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

• A. Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
• B. Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
• C. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
• D. Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
• E. Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.