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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame named 'transactions' containing transaction data'. You need to create a UDTF using Python to categorize transactions into 'High Value', 'Medium Value', and 'Low Value' based on the transaction amount and the customer's region. The categorization logic requires access to a dynamically updated lookup table stored in a Snowflake stage. Which approach would be MOST efficient and scalable, minimizing data transfer and maximizing Snowpark's vectorized operations?

A) Create a vectorized UDF. Load the lookup table from the stage into the UDF's environment once during initialization. Then, process transactions in batches using pandas DataFrames within the UDF.
B) Use a UDTF with the parameter, reading the lookup table directly into the UDTF using a Snowpark DataFrame and joining it with each batch of the 'transactions DataFrame. Materialize the result to a temporary table.
C) Define a scalar UDF that queries the lookup table directly from Snowflake using a Snowflake connector. This avoids data transfer to the UDF but introduces external dependency and connection management overhead for each row.
D) Create a vectorized UDTF that loads the lookup table into memory during the first call, and then caches it for subsequent calls. Implement a refresh mechanism using a Snowflake external function triggered by stage updates.
E) Use a scalar UDF, reading the lookup table from the stage for each transaction. This ensures data consistency but may incur significant overhead for each row processed.

2. You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?

A)

B)

C)

D)

E)

3. Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
B) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
C) The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
D) The code reads data from a table named 'my_table' in Snowflake.
E) The code establishes a connection to Snowflake using the provided credentials.

4. You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?

A) Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.
B) Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
C) Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.
D) Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
E) Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.

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

Solutions: