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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a Snowpark application to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS'. The application should be deployed as a UDF. The sentiment analysis is performed by a third-party Python library, 'sentiment_analyzer'. Due to security constraints, direct internet access is prohibited from within the Snowflake environment. What steps are necessary to ensure the 'sentiment_analyzer' library can be used by your Snowpark UDF?

A) Use the 'packages' parameter in the UDF creation statement to specify the 'sentiment_analyzer' library from Anaconda.
B) Install the 'sentiment_analyzer' library using 'conda install' directly within the Snowpark session before creating the UDF.
C) Request Snowflake support to whitelist the 'sentiment_analyzer' library for direct download during UDF execution.
D) Package the 'sentiment_analyzer' library into a JAR file and upload it to a Snowflake stage, then specify the JAR in the 'imports' parameter of the UDF creation statement.
E) Package the 'sentiment_analyzer' library into a ZIP file and upload it to a Snowflake stage, then specify the ZIP in the 'imports' parameter of the UDF creation statement.

2. You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?

A) Use the 'CREATE OR REPLACE JAVA FUNCTION' command directly in Snowsight and paste the Java code along with the dependencies' contents into the function body.
B) Compile the Java code into a native library (e.g., a .so file), upload it to a Snowflake stage, and use the 'CREATE EXTERNAL FUNCTIONS command to invoke it.
C) Package the Java code and dependencies into a JAR file and upload it to a Snowflake stage. Use the 'CREATE FUNCTION' command with the 'IMPORTS' clause to reference the JAR file.
D) Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake
E) Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.

3. You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A) Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
B) Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
C) Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
D) Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
E) Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

4. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.
B) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.
C) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
D) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
E) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.

5. You are tasked with optimizing a Snowpark application that performs complex geospatial calculations on a large dataset of location coordinates. The application is currently running on a standard Snowflake warehouse. Initial tests indicate that the application is CPU- bound. Which of the following actions would be MOST effective in improving the performance of this Snowpark application?

A) Switching to a larger Snowflake warehouse size (e.g., from X-Small to Small) with the same warehouse type.
B) Partitioning the location data based on latitude and longitude and leveraging Snowpark's DataFrame API for filtering data before geospatial calculations.
C) Switching to a Snowpark-optimized warehouse and increasing the warehouse size.
D) Increasing the value of parameter at the account level.
E) Enable result caching and reduce the amount of data being processed

Solutions: