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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A) Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
B) Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
C) The 'eager_execution' session parameter is set to 'True'.
D) The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
E) Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.

2. You are working with image files stored in a Snowflake internal stage named 'image_stage'. You need to write a Snowpark Python application to resize these images using a Python library called 'PIG. The resizing logic is encapsulated in a function called resize_image(snowflake_file: SnowflakeFile, width: int, height: int) -> bytes. Which of the following code snippets correctly registers he 'resize image' function as a UDF and applies it to the image files?

A)

B)

C)

D)

E)

3. A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A)

B)

C)

D)

E)

4. Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

A) Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.
B) Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.
C) Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
D) Reduce the 'window_size' parameter. Smaller windows inherently require less computation.
E) Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.

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) Create separate temporary tables in Snowflake for each CTE using SQL, then create Snowpark DataFrames from these temporary tables using session.table(table_name)'.
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) 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) Re-write all CTEs using Snowpark's DataFrame API directly, avoiding the use of 'session.sql()' altogether.

Solutions: