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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowflake view named 'SALES SUMMARY VW' that joins several large tables and performs complex aggregations. You need to create a Snowpark DataFrame from this view Which of the following considerations are MOST important to ensure optimal performance and resource utilization when working with this DataFrame?

A) Always specify the schema explicitly when creating the DataFrame from the view to prevent Snowpark from inferring it.
B) Avoid calling 'collect()' or 'toPandas()' on the entire DataFrame unless absolutely necessary, as these operations bring all data into the client's memory.
C) Materialize the view into a temporary table using 'CREATE TEMPORARY TABLE AS SELECT FROM SALES SUMMARY VW before creating the Snowpark DataFrame.
D) Use 'session.sql()' with the view's definition to create the DataFrame, as this is generally faster than 'session.table()' for views.
E) Leverage Snowpark's lazy evaluation and pushdown optimization capabilities by performing transformations and filtering on the DataFrame before executing actions.

2. You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

A) The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.
B) The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.
C) The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.
D) The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.
E) The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.

3. When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

A) Regularly rotate the key pair.
B) Encrypt the private key at rest.
C) Grant broad access to the environment variable containing the private key to all developers.
D) Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
E) Store the private key directly in the Snowpark code repository.

4. You have developed a Snowpark application that uses a Python UDF to perform sentiment analysis on text data extracted from JSON files stored in a Snowflake stage. The UDF relies on a large pre-trained machine learning model that is loaded during the UDF initialization. After deploying the application, you observe that the UDF initialization is taking a significant amount of time, causing slow query performance. What are the three MOST effective strategies to optimize the UDF initialization time in this scenario?

A) Use the 'cachetools' library to cache the loaded model within the UDF. This will help to avoid reloading the model every time the UDF is called.
B) Use the 'snowflake.snowpark.files.SnowflakeFile' class to load the model directly from the Snowflake stage within the UDF initializer, but only if the model is smaller than 256M
C) Use the 'streamlit' library and its caching capabilities to cache loaded models. The UDF should call the streamlit api to retrieve the already loaded model.
D) Load the model outside the UDF definition within the Snowpark session, pass it as an argument to the UDF, then use the model as part of a vectorized UDF.
E) Utilize the 'context.add_dependency' method in Snowpark to specify the model file as a dependency. Snowflake will automatically distribute and cache the model file to the worker nodes.

5. You have a Snowpark DataFrame named 'sales df containing sales data for different products. The DataFrame includes columns product_id' (INTEGER), 'sale_date' (DATE), 'quantity' (INTEGER), and 'price' (FLOAT). You need to calculate the total revenue for each product on a monthly basis and store the result in a new DataFrame named Which of the following Snowpark code snippets will correctly achieve this, while maximizing performance and minimizing data shuffling?

A) ...python from snowflake.snowpark.functions import date_part, sum monthly_revenue_df = sales_df.groupBy('product_id', date_part('month', 'sale_date').alias('sale_month')).agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
B) ...python from snowflake.snowpark.functions import monthname, sum monthly_revenue_df = sales_df.groupBy('product_id', monthname('sale_date').alias('sale_month')).agg(sum(sales_dfl'quantity'] sales_dfl'price']).alias('total_revenue'))
C) ...python from snowflake.snowpark.functions import date_format, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_format(sales_df['sale_date'], 'yyyy-MM')).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
D) ...python from snowflake.snowpark.functions import month, sum monthly_revenue_df = sales_df.groupBy('product_id', month('sale_date').alias('sale_month')).agg(sum(sales_df['quantity'l sales_df['price']).alias('total_revenue'))
E) ...python from snowflake.snowpark.functions import to_date, date_trunc, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_trunc('MM', sales_df['sale_date'])).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))

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