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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called 'RAW SENSOR DATA'. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named 'ENRICH SENSOR DATA, is written in Python and resides in a stage called 'UDF STAGE. The UDF takes three arguments: 'timestamp', and 'raw_value', all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

A)

B)

C)

D)

E)

2. You are using Snowpark Python to process a large dataset. You need to persist a DataFrame to a Snowflake table but want to ensure the operation is as efficient as possible and minimizes the data transfer overhead. The table already exists with the appropriate schema. Which of the following strategies would be the MOST efficient way to write the DataFrame to the existing table?

A) Use 'df.write.insertlnto('existing_table')'.
B) Create a temporary table, load the DataFrame into it using , and then use a 'CREATE OR REPLACE TABLE AS SELECT FROM temp_table' statement.
C) Write the DataFrame to a stage location as Parquet files and then use a 'COPY INTO existing_table FROM command.
D) Use 'df.write.mode('overwrite').saveAsTable('existing_table'Y.
E) Use with the default save mode (which is 'append').

3. You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A) The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
B) The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.
C) The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.
D) The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.
E) The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.

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) Using Python type hints for input parameters and relying on Snowpark's implicit type inference for the DataFrame return value.
E) Explicitly registering the stored procedure with 'sproc.registeff and specifying the input types using 'Argument objects and the return type using with schema definition.

5. You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A) Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.
B) Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
C) Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
D) Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
E) Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.

Solutions: