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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame named 'transactions' containing transaction data'. You need to create a UDTF using Python to categorize transactions into 'High Value', 'Medium Value', and 'Low Value' based on the transaction amount and the customer's region. The categorization logic requires access to a dynamically updated lookup table stored in a Snowflake stage. Which approach would be MOST efficient and scalable, minimizing data transfer and maximizing Snowpark's vectorized operations?

A) Define a scalar UDF that queries the lookup table directly from Snowflake using a Snowflake connector. This avoids data transfer to the UDF but introduces external dependency and connection management overhead for each row.
B) Use a scalar UDF, reading the lookup table from the stage for each transaction. This ensures data consistency but may incur significant overhead for each row processed.
C) Use a UDTF with the parameter, reading the lookup table directly into the UDTF using a Snowpark DataFrame and joining it with each batch of the 'transactions DataFrame. Materialize the result to a temporary table.
D) Create a vectorized UDTF that loads the lookup table into memory during the first call, and then caches it for subsequent calls. Implement a refresh mechanism using a Snowflake external function triggered by stage updates.
E) Create a vectorized UDF. Load the lookup table from the stage into the UDF's environment once during initialization. Then, process transactions in batches using pandas DataFrames within the UDF.

2. You have a Snowpark Python stored procedure that performs complex data transformations. This stored procedure needs to read data from a large table ('TRANSACTIONS) and write the transformed data to another table PROCESSED TRANSACTIONS'). You want to optimize the performance of this stored procedure by leveraging Snowpark's features for parallel processing. Which of the following approaches can significantly improve the performance of the stored procedure, assuming sufficient warehouse resources are available?

A) Use the Snowpark DataFrame API to read the 'TRANSACTIONS' table and apply transformations using vectorized UDFs. Then, use the 'write' method to write the transformed data to the 'PROCESSED TRANSACTIONS' table.
B) Read the entire 'TRANSACTIONS table into a Pandas DataFrame within the stored procedure and perform the transformations using Pandas functions. Then, write the transformed data back to the table using Snowpark's 'createDataFrame' and 'write' methods.
C) Use Snowflake's standard SQL queries within the stored procedure to read and transform the data. Write the results to the 'PROCESSED TRANSACTIONS table using 'INSERT statements.
D) Use Snowpark's 'sprocs decorator with appropriate 'packages' and leverage the Snowpark DataFrame API with vectorized UDFs to transform the data. Use 'session.write_pandaS to write the Pandas DataFrame to the 'PROCESSED_TRANSACTIONS' table after the transformation.
E) Load the data from 'TRANSACTIONS' table into a temporary table within the stored procedure, then use standard SQL queries on the temporary table for transformations, finally using snowpark DataFrame API to write it back to the 'PROCESSED_TRANSACTIONS' table.

3. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
B) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.
C) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.
D) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
E) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.

4. You are using Snowpark for Python to process a large dataset of website clickstream data'. The dataset contains columns such as 'session_id', 'user_id', 'timestamp', 'page_url', and 'event_type' (e.g., 'click', 'pageview', 'purchase'). You want to identify fraudulent user sessions based on the following criteria: A user session is considered fraudulent if it contains more than 100 clicks within a I-minute window. A user session is considered fraudulent if it contains more than 5 purchase events within a 5-minute window. Which of the following code snippets demonstrates the most efficient way to identify fraudulent sessions using Snowpark for Python? Select two that apply.

A)

B)

C)

D)

E)

5. A data engineering team is developing a Snowpark application to process large volumes of data'. They aim to leverage session parameters for fine-grained control over query execution and resource allocation. Which of the following methods is the MOST efficient and secure way to set session parameters, ensuring that sensitive information like warehouse size and query timeouts are dynamically adjusted based on the workload without hardcoding values in the application?

A) Leveraging Snowflake's parameter hierarchy by setting account-level parameters and inheriting them into the Snowpark session.
B) Using the 'snowsqr CLI tool to pre-configure session parameters before running the Snowpark application.
C) Directly using 'session.sql('ALTER SESSION SET QUERY _ TIMEOUT = for each session.
D) Utilizing Snowpark session builder to set parameters using a dictionary read from a secure configuration file, then overriding defaults based on workload characteristics. Example: 'session = Session.builder.configs(config).config('warehouse', workload_optimized_warehouse).create()'
E) Using environment variables to store parameter values and accessing them via 'os.environ['WAREHOUSE SIZET within the Snowpark application.

Solutions: