Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
B) 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.
C) First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
D) 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.
E) 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.

2. A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
B) Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
C) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
D) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.
E) Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.

3. A data engineer is tasked with transforming a large dataset of customer transactions using Snowpark Python. The dataset contains personally identifiable information (PII) that needs to be masked before further analysis. They decide to use a UDF to perform the masking. Consider the following Python UDF:

The engineer registers this UDF and attempts to apply it to a column named 'customer email' in a Snowpark DataFrame named 'customer data'. Which of the following code snippets is the MOST efficient and secure way to apply this UDF and replace the 'customer email' column with the masked values?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A) Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.
B) Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.
C) Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.
D) Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.
E) Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.

5. You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A) Increase the warehouse size used for the Snowpark session to provide more computational resources.
B) Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.
C) Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.
D) Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
E) Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.

Solutions: