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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A) Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
B) Use to explicitly declare the Pandas dependency.
C) Change 'FloatType' to 'DoubleType' as it provides more precision.
D) Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
E) Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

2. You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

A) There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.
B) The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.
C) An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
D) The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.
E) Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.

3. A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

A) Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.
B) Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.
C) Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.
D) Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.
E) Implement a Streamlit application that calls the stored procedure when a button is pressed.

4. A data engineer is tasked with creating a Snowpark session using JWT authentication. They have a private key 'rsa_key.pff, a user name 'snowpark_user' , and an account identifier 'my_account'. The goal is to create a session object suitable for submitting Snowpark jobs. Which code snippet correctly demonstrates the instantiation of a session object using JWT?

A)

B)

C)

D)

E)

5. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) The 'count? operation will use the cached results of and apply an additional filter on the cached data.
B) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
C) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
D) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
E) The 'countl' operation will trigger the materialization and caching of 'filtered_df.

Solutions: