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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key as an environment variable within the Snowflake warehouse configuration.
B) Store the API key as a constant string within the stored procedure's code.
C) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
D) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
E) Store the API key in a Snowflake table and query it within the stored procedure.

2. You are tasked with building a machine learning pipeline in Snowpark to predict customer churn. You plan to use the scikit-learn library for model training and want to deploy the trained model as a Snowpark UDF for real-time scoring. Consider the following code snippet:

A) The code will fail because the 'moder object cannot be directly serialized and passed to the UDE A different serialization method (e.g., pickle) is required.
B) The code will execute successfully and create a UDF that predicts churn using the trained model.
C) The code will fail because the return type of the UDF is not explicitly defined. Snowpark requires explicit type hints for UDF return values.
D) The code will fail because scikit-learn is not a supported library for Snowpark UDFs by default, and needs to be explicitly added to the session imports.
E) The code will fail because the trained model needs to be saved to a stage and loaded within the UDF, rather than being passed directly.

3. You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?

A) ...python import snowflake.snowpark.functions as sf df_transformed = df.with_column('transaction_date', sf.to_date(sf.col('transaction_date'), format='YYYY- MM-DD')).with_column('transaction_amount', sf.to_double(sf.regexp_replace(sf.col('transaction_amount'), '[$,]', ")
B) ...python df_transformed = df.select(to_date(col('transaction_date')).alias('transaction_date'), to_double(col('transaction_amount')).alias('transaction_amount'))
C) ...python from snowflake.snowpark.functions import try_to_date, try_to_double df_transformed = df.with_column('transaction_date', try_to_date(col('transaction_date'))).with_column('transaction_amount', try_to_double(regexp_replace(col('transaction_amount'), '[$,]', ")))
D) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date'))).with_column('transaction_amount', to_double(col('transaction_amount').replace('$',").replace(',',")))
E) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date') YYYY-MM-DD')).with_column('transaction_amount', col('transaction_amount').cast(DoubleType()))

5. A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

A) Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
B) Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
C) Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
D) Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
E) Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.

Solutions: