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High passing rate and hit rate to guarantee the client pass the exam

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3 versions for the client to choose

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.

A) Disable security features to avoid dependency conflicts.
B) Utilize Snowflake's managed dependencies to handle packages installation and versioning automatically. Register the UDF to run in a secure execution environment within Snowflake.
C) Include the third-party library's code directly within the UDF's code.
D) Use Snowflake's Anaconda channel to manage dependencies and specify the required packages in the UDF's registration using 'packages' argument.
E) Deploy the UDF as an external function and manage dependencies using the external function's environment.

2. A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.

A)

B)

C)

D)

E)

3. You are developing a Snowpark application using Visual Studio Code and the Snowflake VS Code extension. You want to configure the extension to automatically detect and use a specific Anaconda environment for your Snowpark development. Assuming you have already created an Anaconda environment named 'snowpark_env', which configuration setting in the VS Code settings.json file would correctly specify the Python path for the Snowflake extension?

A) "python.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
B) "python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
C) "snowflake.python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
D) "snowflake.snowpark.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
E) "snowsql.pythonPath": "/path/to/anaconda3/envs/snowpark_env/bin/python"

4. You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

A)

B)

C)

D)

E)

5. Consider the following scenario: You need to implement a UDF in Snowpark Python to calculate the distance between two geographical coordinates (latitude and longitude). The UDF should handle potential null values gracefully and return null if either input coordinate is null. Which code snippet demonstrates the MOST efficient and correct implementation, leveraging Snowpark's capabilities?

A)

B)

C)

D)

E)

Solutions: