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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 allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
B) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
C) To enable the stored procedure to be called from other programming languages besides Python.
D) To improve the performance of the stored procedure by enabling compile-time optimizations.
E) 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.

2. You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A) Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
B) Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
C) Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
D) Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
E) Create separate functions for each file type and location combination (e.g.,

3. 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)

4. Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

A)

B)

C)

D)

E)

Solutions: