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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

A)

B)

C)

D)

E)

2. A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

A) Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
B) Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
C) Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
D) Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
E) Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.

3. You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A) Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.
B) Increase the warehouse size to the largest possible option.
C) Use the function to sort the data before performing the transformations.
D) Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.
E) Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.

4. You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A) Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
B) Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
C) Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
D) Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
E) Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.

5. You are using Snowpark to process a DataFrame 'employee df containing employee data, including 'employee_id', 'name' , 'department' , and 'salary'. You need to implement a complex data cleaning and transformation pipeline that involves the following steps: 1. Remove duplicate rows based on 'employee id'. 2. Fill missing 'salary' values with the average salary for the employee's department. 3. Standardize department names by converting them to uppercase. 4. Create a new column 'salary_range' based on the salary. if Salary less than 50k 'Low', greater than 50k and less than 100k 'Medium', greater than 100k 'High'. Which of the following code snippets MOST effectively combines these transformations into a single, readable, and efficient Snowpark pipeline? Assume you have a session object available named 'session' and import necessary modules from 'snowflake.snowpark.functions as F'

A)

B)

C)

D)

E)

Solutions: