Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

A) Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
B) Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.
C) Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
D) Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
E) Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.

2. You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply

A) Create a custom Anaconda environment with the required packages and upload it to a Snowflake stage. Then, specify the stage location in the 'imports' argument of the 'CREATE PROCEDURE statement.
B) Bundle the required libraries into a ZIP file and upload it to a Snowflake stage. Then, add the ZIP file to the 'imports' list during stored procedure creation, ensuring that the library paths are correctly referenced within the Python code.
C) Use session.custom_package to resolve dependencied
D) Specify the required packages in the 'packages' argument of the '@sproc' decorator or 'session.add_packageS method.
E) Include the necessary 'import' statements for the libraries within the stored procedure's code. Snowflake will automatically resolve and install the dependencies.

3. You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

A)

B)

C)

D)

E)

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

5. You are developing a Snowpark application that performs feature engineering on a dataset of customer transactions. This involves calculating several complex aggregate features such as rolling averages, medians, and custom ratios. You want to optimize the performance of this feature engineering process using a Snowpark-optimized warehouse. Which of the following strategies would be MOST effective in achieving optimal performance?

A) Use the 'GROUP BY clause in Snowpark SQL to compute aggregate features, leveraging window functions where appropriate for rolling calculations.
B) Implement all feature engineering calculations using Python User-Defined Functions (UDFs) and apply them to the Snowpark DataFrame.
C) Use stored procedures implemented in Java within Snowpark for feature calculations.
D) Materialize intermediate Snowpark DataFrames after each feature engineering step to avoid recomputation.
E) Leverage Snowpark's built-in functions and SQL expressions as much as possible for feature engineering, and rewrite performance-critical calculations as Java or Scala User-Defined Table Functions (UDTFs).

Solutions: