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Snowflake SPS-C01 Exam Syllabus Topics:

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

2. You are working with Snowpark and a DataFrame named 'orders df that contains order data, including a column named 'items' which is a VARIANT type and holds an array of JSON objects, where each object represents an item in the order. You need to explode this array into separate rows, extracting the 'item_id' and 'quantity' for each item. Which of the following Snowpark snippets correctly performs this transformation AND handles potential NULL or empty arrays in the 'items' column?

A)

B)

C)

D)

E)

3. You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)

A)

B)

C)

D)

E)

4. You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.

A) Use Snowflake's Anaconda channel to manage dependencies and specify the required packages in the UDF's registration using 'packages' argument.
B) Disable security features to avoid dependency conflicts.
C) Utilize Snowflake's managed dependencies to handle packages installation and versioning automatically. Register the UDF to run in a secure execution environment within Snowflake.
D) Deploy the UDF as an external function and manage dependencies using the external function's environment.
E) Include the third-party library's code directly within the UDF's code.

5. A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

A) Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.
B) Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety
C) Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.
D) Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
E) Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.

Solutions: