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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?

A) Repartition the 'transactions DataFrame by the 'customer_id' column and the 'customers' DataFrame by the 'id' column before performing the join using 'df_transactions.repartition('customer_id').join(df_customers.repartition('id'), df_transactions['customer_id'] == df_customers['id'])'.
B) Increase the size of the Snowpark-optimized warehouse to provide more memory and CPU resources for the join operation.
C) Explicitly specify the join type as a broadcast join using a hint: 'df_transactions.join(df_customers, df_transactions['customer_id'] == df_customers['id'],
D) Broadcast the smaller DataFrame (customers') to all nodes in the warehouse before performing the join. Use: 'from snowflake.snowpark.functions import broadcast; df_transactions.join(broadcast(df_customers), df_transactions['customer_id']
E) Ensure that the 'customer_id' column in the 'transactions' table and the 'id' column in the 'customers' table have appropriate indexes defined in Snowflake before performing the join.

2. Consider the following Snowpark Python code snippet:

A) The 'upper()' function will be executed on the client-side (where the Python code is running) for each row in the 'customers' table.
B)

C) The code demonstrates the Snowpark architecture, where transformations are translated into SQL and executed in Snowflake's engine. Only the final 'collect()' brings the results back to the client.
D) This code requires a configured Anaconda environment to run successfully.
E) The function will retrieve all rows from the 'customers' table and store them in a local Pandas DataFrame before applying the function.

3. You need to perform a set difference operation between two DataFrames in Snowpark Python. 'dfl' contains customer IDs from a marketing campaign, and 'df2 contains customer IDs from a recent purchase event. You want to identify customers who were targeted in the campaign but did not make a recent purchase. Both DataFrames have a column named 'customer id'. Which of the following approaches provides the most efficient way to accomplish this task in Snowpark?

A)

B)

C)

D)

E)

4. You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

A) Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.
B) Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
C) Manually partitioning the DataFrame into smaller chunks before applying transformations.
D) Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.
E) Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.

5. You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

A)

B)

C)

D)

E)

Solutions: