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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A) Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
B) Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
C) Disable automatic query optimization features in Snowflake to gain more control over query execution.
D) Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
E) Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).

3. You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

A) python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
B) 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)
C) python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
D) 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])
E) python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)

4. Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A) 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.
B) 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.
C) 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.
D) 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.
E) 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.

5. You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?

A)

B)

C)

D)

E)

Solutions: