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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?

A)

B)

C)

D)

E)

2. 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) 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.
B) 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.
C) 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.
D) 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.
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.

3. You are building a Snowpark application that involves a UDF. Consider that you are creating UDF as follows:

A) The behaviour of the UDF registration dependends on the size of code in it.
B) There will be no attempt to register UDF until .collect()' action is performed.
C) The UDF is registered lazily; only when called the first time will it be registered and available in the session.
D) UDF registration occurs on the server-side, only when a dataframe action such as collect or show is called .
E) UDF registration happens on the client side, before dataframe transformations are evaluated and sent to Snowflake.

4. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

5. You are developing a Snowpark application that uses a UDF written in Python. This UDF requires several third-party Python packages (e.g., 'pandas', 'scikit-learn'). You need to ensure these packages are available in the UDF's execution environment within Snowflake. You have the packages listed in a 'requirements.txt' file. Which of the following methods, alone or in combination, correctly provision these dependencies for the UDF? (Select all that apply)

A) Use the 'session.add_packages' method to add the packages programmatically before registering the UDF.
B) Include the packages directly in the '@udf decorator's 'packages' argument as strings (e.g., 'scikit-learnT).
C) Manually install the packages on the Snowflake compute warehouse nodes using SSH.
D) Upload the 'requirements.txt file to a Snowflake stage and specify the stage location in the 'imports' argument of the '@udf decorator.
E) Create a Conda environment with the required packages and upload the resulting environment file to a Snowflake stage. Specify the stage location in the 'conda_integration' argument of the 'session.udf.register' method or guff decorator.

Solutions: