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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You are tasked with creating a Snowpark DataFrame from a series of large Parquet files stored in an external stage 'my_stage' . The files contain customer transaction data, but some files are corrupted and cause errors during DataFrame creation. You want to implement a solution that skips the corrupted files and logs the filenames of those files to a table named 'failed_files'. Assuming you have a Snowpark session 'session' and a UDF that inserts filenames into the 'failed_files' table, which of the following approaches is the MOST efficient and robust way to achieve this, while minimizing impact on performance and maintaining data integrity? Consider that you don't have direct control over the file format and data quality within the stage.

A) Use the command in Snowflake to load the Parquet files into a temporary table, specifying the 'ON ERROR = CONTINUE option. Then, create a Snowpark DataFrame from the temporary table. Log any rejected files using a 'VALIDATION MODE = RETURN ERRORS' copy command before creating the temporary table.
B) Implement a custom file listing function using 'session.sql('LlST to identify potentially corrupted files by checking file size or metadata, then exclude these files when creating the Snowpark DataFrame. Use 'session.read.parquet' with the filtered list of files.
C) Use 'session.read.parquet('stage://my_stage/ within a try-except block to catch errors. Inside the 'except' block, call with the filename. Retry the read operation for remaining files after removing the failing file from stage.
D) Create a Snowpark DataFrame using 'session.read.option('mode', 'PERMISSIVE').parquet('stage://my_stage/ ')'. This automatically skips corrupted records within valid files but doesn't handle entire corrupted files. Afterward, compare the counts of each file before and after processing to identify corrupted files based on lost records.

3. You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

A) Use 'df.checkpoint()' to truncate the DataFrame lineage. This will prevent re-computation in any downstream operations. Monitor the impact on storage costs.
B) Using a local variable to store the DataFrame. This method is most suitable for materializing the results of the DataFrame.
C) Create a temporary table using 'df.write.save_as_table('temp_transactions', temporary-True)'. This persists the DataFrame to Snowflake storage, reducing the need for repeated computations. Monitor the size of the temporary table and its impact on storage costs.
D) Use to materialize the DataFrame in memory. This is the most efficient approach as it minimizes disk I/O. Consider the size of the DataFrame relative to available memory to avoid memory pressure.
E) Write the DataFrame to a persistent Snowflake table using and then read it back into a new DataFrame. This ensures data persistence but may introduce overhead due to data serialization and deserialization. Only use this method if persistence is required beyond the session.

4. You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A)

B)

C) Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.
D) Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.
E) Require all users to remove the password protection from their private keys to simplify the session creation process.

5. A data engineering team is migrating a series of complex SQL queries into Snowpark Python to leverage vectorized UDFs and optimize performance. They currently use several Common Table Expressions (CTEs) within their SQL queries. What is the most efficient and Pythonic approach to create a Snowpark DataFrame representing the result of a complex SQL query with multiple CTEs, minimizing code redundancy and maintaining readability?

A) Concatenate the SQL statements representing each CTE and the final SELECT statement into a single long string, then use to create the DataFrame.
B) Use the method to create separate Snowpark DataFrames for each CTE and then use Snowpark DataFrame joins to combine them into the final DataFrame.
C) Create separate temporary tables in Snowflake for each CTE using SQL, then create Snowpark DataFrames from these temporary tables using session.table(table_name)'.
D) Utilize the method to create a single Snowpark DataFrame by executing the entire SQL query with CTEs. Then, use Snowpark's DataFrame API for further transformations if needed.
E) Re-write all CTEs using Snowpark's DataFrame API directly, avoiding the use of 'session.sql()' altogether.

Solutions: