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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You need to perform a set difference operation between two DataFrames in Snowpark Python. 'dfl' contains customer IDs from a marketing campaign, and 'df2 contains customer IDs from a recent purchase event. You want to identify customers who were targeted in the campaign but did not make a recent purchase. Both DataFrames have a column named 'customer id'. Which of the following approaches provides the most efficient way to accomplish this task in Snowpark?

A)

B)

C)

D)

E)

2. 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 Pipes to ingest data and trigger the stored procedure based on new data arrival.
B) Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.
C) Implement a Streamlit application that calls the stored procedure when a button is pressed.
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) 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.

3. You are tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?

Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?

A) Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.
B) Bypass role-based access control and use shared credentials for UDF execution.
C) Grant global USAGE privilege on the function's stage location to all users.
D) Store the encryption key directly in the UDF code for simplicity.
E) Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.

4. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
B) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
C) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.
D) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
E) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.

5. You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.

A) Modify the UDF to process data in smaller batches using a generator pattern, reducing memory consumption at any given time. Deploy the UDF with the same warehouse size.
B) Increase the warehouse size to the largest available option. This will provide more memory to the UDE
C) Implement the Python UDF as a Snowpark Stored Procedure. Deploy the UDF with the same warehouse size.
D) Implement a caching mechanism within the UDF to store intermediate results and reduce the number of calls to the external library. Deploy the UDF with the same warehouse size.
E) Rewrite the UDF in Java using Snowpark API, which generally has a smaller memory footprint than Python. Deploy the UDF with the same warehouse size.

Solutions: