Snowflake DSA-C03 Value Pack (Frequently Bought Together)

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. 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.
• B. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
• C. 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.
• D. 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.
• E. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.

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. Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
• B. Calculate the average purchase frequency across all customers using and group by 'customer_id'.
• C. Create a UDF that computes the purchase frequency for a single user and apply it to all customers.
• D. Use the window function to divide customers into quantiles based on their total purchase count.
• E. 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.

A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

• A. Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
• B. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
• C. Add '@F.sproc' decorator before the function definition.
• D. Redefine the function to accept string arguments and cast them to the correct data types within the function.
• E. Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),

You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

• A. The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.
• B. The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.
• C. The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.
• D. The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.
• E. There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.

You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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