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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a new Snowflake table. The transformations involve complex calculations and aggregations. During testing, you observe that the application is consuming a significant amount of credits. Which of the following optimization strategies would be MOST effective in reducing the credit consumption of your Snowpark application?

A. Convert all Python User-Defined Functions (UDFs) to Java User-Defined Table Functions (UDTFs) for improved performance.
B. Minimize the amount of data transferred between Snowpark and Snowflake by pushing down transformations and using stored procedures where appropriate.
C. Use the 'cache()' method on intermediate Snowpark DataFrames to avoid recomputation of transformations.
D. Disable auto-scaling on the Snowpark-optimized warehouse to prevent it from scaling up unnecessarily.
E. Optimize the SQL queries generated by Snowpark by explicitly specifying join hints and using appropriate indexes.

Question 2

You are tasked with building a machine learning pipeline in Snowpark to predict customer churn. You plan to use the scikit-learn library for model training and want to deploy the trained model as a Snowpark UDF for real-time scoring. Consider the following code snippet:

A. The code will fail because the 'moder object cannot be directly serialized and passed to the UDE A different serialization method (e.g., pickle) is required.
B. The code will fail because scikit-learn is not a supported library for Snowpark UDFs by default, and needs to be explicitly added to the session imports.
C. The code will fail because the trained model needs to be saved to a stage and loaded within the UDF, rather than being passed directly.
D. The code will fail because the return type of the UDF is not explicitly defined. Snowpark requires explicit type hints for UDF return values.
E. The code will execute successfully and create a UDF that predicts churn using the trained model.

Question 3

You have a Snowpark DataFrame containing customer transaction data'. Your goal is to save this DataFrame as a set of Parquet files in an existing Snowflake stage named , partitioned by the 'transaction_date' column. You want to ensure that the files are automatically compressed using the Zstandard codec and that existing files with the same name are overwritten. Which of the following Snowpark code snippet achieves this with the most optimal approach and respects best practices?

A. Option C
B. Option B
C. Option A
D. Option E
E. Option D

Question 4

You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?

A.

B.

C.

D.

E.

Question 5

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. First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
C. 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.
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. 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.

Solutions: