Free Snowflake SPS-C01 Exam Dumps Questions & Answers

You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

• A.The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
• B.The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
• C.The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.
• D.The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'
• E.The Anaconda environment is not activated. Activate the environment using 'conda activate

You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

• A.Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.
• B.Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
• C.Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
• D.Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
• E.Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.

You have a requirement to create Snowpark DataFrames from CSV files located in an AWS S3 external stage. Some CSV files have a header row, while others do not. The files also use different delimiters (comma, semicolon, or tab). You want to create a single function that can handle all these variations, without creating separate functions for each combination. The 'create_dataframe' function receives the stage path, the delimiter, and a boolean indicating whether a header is present. Which of the following code snippets, when implemented inside the function, BEST achieves this goal using the Snowpark Python API? Assume a Snowpark session 'session'.

• A.
• B.
• C.
• D.
• E.

You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

• A.Use a single 'ArrayType' to represent the entire dictionary. 'Integer Type').
• B.Define a 'StructType' with ' StructFielcf for each metric, specifying the appropriate data type (e.g.,
• C.Use a single 'VariantType' to represent the entire dictionary.
• D.Define the return type as 'StringType' and serialize the dictionary to JSON within the UDF.
• E.Use a 'MapType' with 'StringType' as the key type and 'VariantType' as the value type.

You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

• A.Broadcast the DataFrame to all compute nodes before applying the UDF.
• B.Convert the DataFrame to a Pandas DataFrame before applying the UDF.
• C.Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
• D.Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
• E.Increase the number of UDF worker threads within the UDF definition.