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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.
B) Configure the Snowpark session with the parameter set to ' FALSE
C) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
D) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
E) Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional

2. You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:

A)

B)

C)

D)

E)

3. A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

A) Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.
B) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.
C) Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.
D) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.
E) Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)

4. You are working with two large Snowpark DataFrames: 'transaction_df and 'product df. 'transaction_df contains transaction data including 'transaction id', 'product id', and 'transaction_date'. 'product df contains product details including 'product id', product_name', and 'product category'. You need to join these DataFrames to analyze transaction data by product category. The 'transaction_df is significantly larger than 'product_df. Which of the following strategies can significantly improve the performance of the join operation in Snowpark? (Select all that apply)

A) Use a broadcast join by explicitly specifying 'broadcast-True in the 'join' function when joining 'product_df to 'transaction_df.
B) Cache the 'transaction_df DataFrame before the join operation using
C) Use a 'hint' to force Snowflake to use a specific join algorithm like 'MERGE JOINS.
D) Filter the 'transaction_df to a smaller subset based on 'transaction_date' before performing the join, if only recent transactions are needed.
E) Ensure that the 'product_id' column in both DataFrames is of the same data type and has statistics collected on it.

5. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.
B) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
C) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.
D) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.
E) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.

Solutions: