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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You are developing a Snowpark Python application that connects to Snowflake using key pair authentication. You have the private key stored securely in an environment variable named 'SNOWFLAKE PRIVATE KEY. Which of the following code snippets correctly establishes a Snowpark session using this method, assuming all other necessary connection parameters (account, user, database, schema, warehouse) are also set as environment variables?

A)

B)

C)

D)

E)

3. Consider the following Snowpark code snippet:

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

A) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
B) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
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 'count? operation will use the cached results of and apply an additional filter on the cached data.

4. Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?

A) Convert the 'product_ids' and 'user_ids' arrays to strings using and before applying 'explode'.
B) Use 'iff()' or 'case when' and and other relevant function to handle exception cases.
C) Use function to dynamically create an array of indices for the 'explode' function and use and function to handle missing values
D) Use two separate 'withColumn()' transformations followed by two 'explode()' operations to flatten each array independently.
E) Use a 'LEFT OUTER JOIN LATERAL FLATTEN' to flatten 'product_ids' and 'user_ids', and then use 'nvl()' or 'coalesce()' to handle cases where one or both arrays are missing.

5. You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

Solutions: