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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To improve the performance of the stored procedure by enabling compile-time optimizations.
B) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
C) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
D) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
E) To enable the stored procedure to be called from other programming languages besides Python.

2. 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) 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.
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) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
D) 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.
E) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.

3. You have a DataFrame 'df in Snowpark representing customer data'. One of the columns, 'customer_details', contains JSON objects with varying structures. Some objects contain 'address' and 'phone' fields, while others only contain 'email'. You need to write a Snowpark query to extract the 'city' from the 'address' field if it exists; otherwise, return NULL. What is the most efficient way to achieve this using the function?

A)

B)

C)

D)

E)

4. You are developing a Snowpark Python UDF to perform sentiment analysis on product reviews. The UDF takes a text review (STRING) as input and returns a sentiment score (FLOAT). You want to operationalize this UDF, ensuring type safety and performance. Which of the following approaches is MOST recommended, considering both ease of use and explicit type declaration?

A) Using Python type hints alone: 'def sentiment_score(review: str) -> float: .... and relying on Snowpark to infer types.
B)

C) Using gudf(return_type=FloatType(), decorator with explicit data type registration.
D) Using only the function name without any explicit type declaration or registration.
E) Casting the result of the UDF to FLOAT within the UDF definition.

5. You have created a Snowpark UDF that uses a custom Python module 'my_module.py', containing a function 'process data'. This module is not available through Anaconda'. You've packaged the module into a zip file named 'my module.zip'. What steps are necessary to deploy this UDF in Snowflake so that it can correctly use the 'my_module'?

A) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'packages' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
B) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
C) Upload 'my_module.zip' to an external stage (e.g., AWS S3 or Azure Blob Storage). Configure Snowflake to access the external stage. Create the UDF, specifying the external stage path in the 'imports' argument.
D) Upload 'my_module.zip' to an internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.
E) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.

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