You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

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You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet:

• A. The code will fail because it does not handle potential security vulnerabilities associated with deserializing pickled objects from untrusted sources.
• B. The code will fail because the 'model_bytes' variable is not accessible within the 'predict' function's scope.
• C. The code will execute successfully. The model serialization and deserialization using pickle are correctly implemented within the stored procedure.
• D. The code will fail because Snowflake stages cannot be used to store model objects.
• E.
  

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A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

• A. Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
• B. Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
• C. Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
• D. Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
• E. Reduce the size of the input DataFrame by sampling the data.

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You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?

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

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A data scientist is building a churn prediction model using Snowflake data'. They want to load a large dataset (50 million rows) from a Snowflake table 'customer_data' into a Pandas DataFrame for feature engineering. They are using the Snowflake Python connector. Given the code snippet below and considering performance and memory usage, which approach would be the most efficient for loading the data into the Pandas DataFrame? Assume you have a properly configured connection and cursor 'cur'. Furthermore, assume that the 'customer id' column is the primary key and uniquely identifies each customer. You are also aware that network bandwidth limitations exist within your environment. ```python import snowflake.connector import pandas as pd # Assume conn and cur are already initialized # conn = snowflake.connector.connect(...) # cur = conn.cursor() query = "SELECT FROM customer data```

• A. ```python with conn.cursor(snowflake.connector.DictCursor) as cur: cur.execute(query) df = pd.DataFrame(cur.fetchall())
• B. ```python cur.execute(query) df = pd.read_sql(query, conn)
• C. ```python import snowflake.connector import pandas as pd import pyarrow import pyarrow.parquet # Enable Arrow result format conn.cursor().execute("ALTER SESSION SET PYTHON USE ARROW RESULT FORMAT-TRUE") cur.execute(query) df =
• D. ```python cur.execute(query) df = pd.DataFrame(cur.fetchall(), columns=[col[0] for col in cur.description])
• E. ```python cur.execute(query) results = cur.fetchmany(size=1000000) df_list = 0 while results: df_list.append(pd.DataFrame(results, for col in cur.description])) results = cur.fetchmany(size=1000000) df = pd.concat(df_list, ignore_index=True)

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