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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a requirement to process a large number of JSON files stored in a Snowflake stage 'json_stage'. These JSON files contain complex nested structures. You need to extract specific fields from these files using Snowpark Python and load them into a Snowflake table. You want to use 'SnowflakeFile' to read the JSON files and minimize the amount of data loaded into memory. Select all that apply from the following options to efficiently accomplish this task:

A. Write a Python script that downloads all JSON files from the stage using 'SnowflakeFile.get' , iterates through the downloaded files, parses each file, extracts the fields, and inserts the data into the Snowflake table using the Snowflake Python connector.
B. Use to directly load all JSON files into a Snowpark DataFrame and then use 'select' with path expressions (e.g., 'col('fieldl .nested_field')) to extract the required fields.
C. Create a UDF that accepts a 'SnowflakeFile' object, opens the file, reads the JSON data using 'json.load', extracts the required fields, and returns a JSON string. Create a Snowpark DataFrame using 'session.read.option('PATTERN', ' then call the UDF with the file path, and finally parse the returned JSON string to load data.
D. Create a UDF that takes a file path as input, constructs a 'SnowflakeFile' object within the UDF, reads the JSON data using 'json.loadS, extracts the required fields, and returns a Row object or a dictionary. Use 'session.sql('SELECT relative_path FROM to get file paths, create Snowpark Dataframe and then call the UDF on each file path.
E. Create a UDTF that accepts a "SnowflakeFile' object, uses 'json.loadS to parse the JSON content incrementally, extracts the desired fields, and yields rows for insertion into the target table. Use 'session.read.option('PATTERN', ' to generate the initial DataFrame.

Question 2

You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?

A.

B.

C.

D.

E.

Question 3

You have a Pandas DataFrame named containing employee information including 'name' , 'department, and You want to create a Snowpark DataFrame named from this Pandas DataFrame and register it as a temporary view named 'TEMP EMPLOYEES. However, you need to ensure that any NULL values in the Pandas DataFrame are handled correctly when creating the Snowpark DataFrame. Which of the following code snippets achieves this, minimizes data transfer and provides best performance considering dataset size is large?

A.

B.

C.

D.

E.

Question 4

You are developing a Snowpark stored procedure to perform data transformation. The procedure needs to handle both integers and strings as input parameters and return a DataFrame. Which of the following is the MOST efficient and recommended way to define the data types for the input parameters and the return value, ensuring proper data handling and type safety during deployment and execution?

A. Defining input parameter types using Python type hints (e.g., 'x: int, y: str') and the return type as 'List[Row]' without explicit registration. Snowpark automatically handles conversion.
B. Using Python type hints for input parameters and explicitly registering the stored procedure using 'sproc.register' and specifying the return type using with schema definition.
C. Relying solely on Snowpark's implicit type inference for both input parameters and the DataFrame return value, avoiding explicit type hints or registration.
D. Explicitly registering the stored procedure with 'sproc.registeff and specifying the input types using 'Argument objects and the return type using with schema definition.
E. Using Python type hints for input parameters and relying on Snowpark's implicit type inference for the DataFrame return value.

Question 5

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. Deploy the UDF as an external function and manage dependencies using the external function's environment.
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. Disable security features to avoid dependency conflicts.

Solutions: