Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to aggregate sales data by region:

During testing, you observe that the performance of this code is suboptimal, especially when dealing with very large 'SALES DATA tables. Using Snowflake's query history, you notice that a significant amount of time is spent on data shuffling during the operation. What optimization strategies could you employ within this Snowpark code to minimize data shuffling and improve the overall performance?

A) Apply a more selective 'filter operation to the 'sales_df' DataFrame before the 'groupBy' operation, reducing the amount of data that needs to be shuffled.
B) Utilize the 'hint' function in Snowpark to provide a join hint that suggests a specific join strategy to the Snowflake query optimizer.
C) store 'SALES_DATX as a clustered table using the 'REGION' column.
D) Increase the warehouse size to provide more resources for data processing, which will inherently reduce data shuffling.
E) Repartition the 'sales_df DataFrame using 'repartitionBy' before the 'groupBy' operation, specifying the 'REGION' column to ensure that data for each region is co-located on the same node.

2. You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?

A) Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.
B) Create a Snowflake stage, upload the Python libraries as .zip files to the stage, and specify the stage path in the '@sproc' decorator's 'imports' parameter.
C) Create a conda environment file ('environment.yml') specifying the dependencies, upload it to a stage, and then use the '@sproc' decorator with the 'packages' argument referencing the conda environment.
D) Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.
E) Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.

3. You are working with Snowpark to create a DataFrame from a Python dictionary where keys represent column names and values are lists representing column data'. However, the dictionary contains lists of varying lengths for different columns. You need to create a DataFrame from the Python dictionary but are unsure how to create it. Which approach should you take and why?

A) Manually pad all lists in the dictionary with 'None' values until they have the same length. Then, create the DataFrame using 'session.createDataFrame(data)'.
B) Transform the dictionary into a list of dictionaries or tuples, padding the short lists with 'None' values. Then, define a schema and use 'session.createDataFrame(data, schema=schema)' to create the DataFrame.
C) DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Snowpark does not support creating DataFrames directly from dictionaries with lists of varying lengths. The code will throw an error. So, manually build the logic of combining the lists.
D) Create a Pandas DataFrame from the dictionary first. Pandas handles lists of unequal lengths by filling the shorter lists with NaN. Then, convert the Pandas
E) Attempt to create the DataFrame directly using 'session.createDataFrame(data)'. Snowpark will automatically pad the shorter lists with 'NULL' values to match the length of the longest list.

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) Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.
B) 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.
C) 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.
D) Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
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 that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A)

B)

C)

D)

E)

Solutions: