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. Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

A) It's not possible to dynamically extract feature names and create columns in Snowpark without knowing the schema in advance.
B) Use the function on the VARIANT column to get an array of feature names. Then, use a loop to iterate over this array and dynamically create new columns using bracket notation (e.g.,
C) Use a User-Defined Function (UDF) to parse the JSON and return a dictionary. Then, use a loop to iterate over the keys in the dictionary and create new columns based on these keys.
D) The function can be used in conjunction with a Snowpark SQL query to dynamically extract the json into separate columns.
E) Use the 'FLATTEN' function within a Snowpark DataFrame transformation. This allows you to transform the key-value pairs within the VARIANT column into separate rows, which can then be pivoted to create new columns.

2. You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame 'customer df with a 'customer name' column. You need to create a new column 'initials' that contains the initials of each customer's name. For example, if 'customer name' is 'John Doe', 'initials' should be 'JD'. You must handle names with multiple words correctly. Which Snowpark SQL expression using the "col()' function is the most efficient and correct way to define the 'initials' column?

A)

B)

C)

D)

E)

5. 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) 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.
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 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
E) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.

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