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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team is developing a Snowpark stored procedure in Python to perform anomaly detection on time-series data stored in a Snowflake table named 'sensor_readingS. The stored procedure needs to efficiently process large volumes of data and return only the rows identified as anomalies. Which of the following approaches would provide the most performant and scalable solution for operationalizing this stored procedure?

A) Execute a SQL query from within the stored procedure using the Snowflake connector for Python to fetch the relevant data, then use a standard Python loop to iterate through the results and apply anomaly detection logic. Return the anomalous rows as a list of dictionaries.
B) Create a UDF with a Scala implementation and use it inside the Snowpark stored procedure to detect anomalies using the Scala implementation for increased processing power.
C) Load the entire 'sensor_readings' table into a Pandas DataFrame within the stored procedure, perform anomaly detection using a Python library like 'scikit-learn' , and then create a Snowpark DataFrame from the filtered Pandas DataFrame to return the results.
D) Use the method to include a pre-trained anomaly detection model (pickled object) in the stored procedure's execution environment. Load the model, use it to predict on the data fetched using 'session.table(Y , and return a Snowpark DataFrame of anomalies.
E) Use the Snowpark API to directly perform anomaly detection calculations (e.g., rolling statistics, z-score calculations) on the 'sensor_readings' table within the stored procedure, leveraging Snowpark's distributed processing capabilities, and then return the resulting Snowpark DataFrame containing only the anomalies.

2. You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.

A) Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.
B) Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.
C) First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.
D) Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.
E) Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.

3. You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

A)

B)

C)

D)

E)

4. Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A) Add in sproc.register.
B) Add 'session.close()' at the end of the main function.
C) Define 'session' as a global variable before calling 'session.sproc.register' .
D) Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.
E) Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.

5. You have a Snowpark DataFrame 'df representing sales data with columns 'product_id', 'region', and 'sales_amount'. You want to calculate the total sales amount for each region. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this?

A)

B)

C)

D)

E)

Solutions: