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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application that reads data from an external stage (AWS S3) and performs several transformations before loading it into a Snowflake table. During testing, you encounter the following error: net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: User does not have OWNERSHIP privilege on integration object 'YOUR INTEGRATION NAME". You have confirmed that the user has the 'USAGE privilege on the integration. Besides granting ownership, which of the following actions could resolve this issue in the MOST secure and efficient way?

A) Modify the integration to include a service principal that the Snowpark application will use to authenticate, eliminating the need for user-level privileges.
B) Grant the 'CREATE TABLE privilege to the user on the database where the target table resides.
C) Use the 'COPY INTO' command with the 'ON_ERROR = CONTINUE option to bypass the permission error.
D) Grant the ' READ privilege on the external stage to the user.
E) Create a custom role with the 'USAGE privilege on the integration and the 'CREATE TABLE' privilege on the database. Grant this role to the user.

2. You are working with a Snowpark DataFrame representing sensor data. The DataFrame contains columns like 'timestamp', 'sensor id' , and 'value'. You need to perform a complex windowing operation to calculate the moving average of the 'value' for each 'sensor id' over a 5-minute window, but only for data points where the 'value' is greater than a threshold. The window should be defined based on the 'timestamp' column. What is the most efficient and correct approach to implement this using Snowpark DataFrames?

A) First apply the moving average calculation to the DataFrame and then filter for rows with values exceeding the threshold, since calculations are performed in order.
B) Create a UDF that takes a list of timestamps and values as input and returns the moving average. Apply this UDF to the entire DataFrame.
C) Use a loop to iterate over each 'sensor_id' , filter the DataFrame for that sensor, calculate the moving average using Pandas windowing functions, and then combine the results.
D) First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
E) Use a combination of 'filter' to apply the threshold condition, 'Window.partitionBy' and 'Window.orderBy' to define the window, and 'avg' window function to calculate the moving average.

3. You are tasked with optimizing a Snowpark application that uses a Python UDF to perform complex string manipulations on a large dataset. The current implementation uses a scalar UDF. You are considering converting it to a vectorized UDF. What are the key considerations and potential limitations you need to address during the conversion to ensure correctness and optimal performance? Choose all that apply:

A) The vectorized UDF's return type must be compatible with Snowpark's data types, and the UDF should return an array of the appropriate type with the same length as the input arrays.
B) Vectorized UDFs always perform better than scalar UDFs, regardless of the complexity of the string manipulations or the size of the dataset.
C) The vectorized UDF should utilize libraries like NumPy or Pandas for efficient array processing, but it's important to be aware of the limitations on available Python packages in the Snowflake environment.
D) The input and output data types of the vectorized UDF must exactly match the corresponding column data types in the Snowpark DataFrame.
E) The vectorized UDF must be able to handle NULL values gracefully within the input arrays, as these can cause errors if not explicitly addressed.

4. You have a Snowpark application that performs machine learning inference on a large dataset of images stored in Snowflake. The inference logic is implemented within a Python UDF that utilizes a pre-trained deep learning model. You notice that the inference process is slow and consumes a significant amount of resources. Which of the following optimization techniques would be MOST effective in improving the performance and reducing the resource consumption of this application?

A) Persist the pre-trained deep learning model in a database table or stage and load it into the UDF's memory during each invocation to avoid repeated loading.
B) Leverage the Snowpark Session object to manage the lifecycle and scope of the pre-trained model within the UDF, ensuring it is loaded only once per session.
C) Optimize the UDF code to use batch processing techniques to process multiple images in a single call, reducing the overhead of UDF invocation.
D) Use external functions instead of UDFs.
E) Ensure the virtual warehouse used by Snowpark is configured with auto-scaling to dynamically adjust the compute resources based on the workload.

5. You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

A) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
B) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.
C) Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.
D) Use 'CREATE OR REPLACE FUNCTION mydb.public.calculate_discount(order_total DOUBLE, customer_segment STRING, promo_code STRING) RETURNS DOUBLE LANGUAGE PYTHON ..: with the updated UDF definition.
E) Use ALTER FUNCTION mydb.public.calculate_discount ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '

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