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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.

A)

B)

C)

D)

E)

2. You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

A)

B)

C)

D)

E)

3. You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A) Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
B) Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
C) Disable automatic query optimization features in Snowflake to gain more control over query execution.
D) Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
E) Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.

4. You have a Snowpark DataFrame named 'customer df containing customer data, including sensitive information like credit card numbers in a column named 'credit card'. You need to persist this data to a Snowflake table named 'secure_customers'. What is the MOST secure and efficient way to achieve this, ensuring that the 'credit card' column is never exposed in plain text during the persistence process and also optimized for subsequent analytical queries?

A) Use a UDF to encrypt the 'credit_card' column before persisting the DataFrame to 'secure_customers' using
B) Create a masking policy in SnoMlake and apply it to the 'credit_card' column in the'secure_customers' table after persisting the 'customer_df using
C) Persist the 'customer_df to a temporary table using 'df.write.mode('overwrite').save_as_table('temp_customers')'. Then, create a new table 'secure_customers' from 'temp_customers' excluding the 'credit_card' column.
D) Persist 'customer_df directly to 'secure_customers' using after dropping the 'credit_card' column using 'df.drop('credit_card')'.
E) Create a Snowpark DataFrame that uses a Secure View to only select the required columns excluding credit_card, and persist that to 'secure_customers' using

5. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

A)

B)

C)

D)

E)

Solutions: