Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01 valid dump

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?

A) Directly translate the Pandas 'apply' operation to a Snowpark 'apply' operation, assuming that Snowpark's implementation is automatically optimized for distributed execution.
B) Utilize Snowpark's Pandas API to seamlessly execute the Pandas code within the Snowflake environment with minimal modifications.
C) Use a stored procedure to execute the pandas 'apply' row by row on the data from snowflake table.
D) Create a Snowpark User-Defined Function (UDF) using Python and apply it to the DataFrame using the 'select method, leveraging Snowflake's distributed execution capabilities.
E) Rewrite the custom function as a vectorized operation using Snowpark DataFrame functions and expressions, avoiding row-by-row processing.

2. You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A) Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation.
B) Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.
C) Using without specifying a storage level. Snowflake will choose a default storage level.
D) Using immediately after the initial DataFrame creation.
E) Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation.

3. 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) 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']
B) Explicitly specify the join type as a broadcast join using a hint: 'df_transactions.join(df_customers, df_transactions['customer_id'] == df_customers['id'],
C) 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'])'.
D) Increase the size of the Snowpark-optimized warehouse to provide more memory and CPU resources for the join operation.
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.

4. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

A)

B)

C)

D)

E)

5. You have a DataFrame 'df in Snowpark representing customer data'. One of the columns, 'customer_details', contains JSON objects with varying structures. Some objects contain 'address' and 'phone' fields, while others only contain 'email'. You need to write a Snowpark query to extract the 'city' from the 'address' field if it exists; otherwise, return NULL. What is the most efficient way to achieve this using the function?

A)

B)

C)

D)

E)

Solutions: