Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 18)

Question 81

You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

A.The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.

B.The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.

C.The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.

D.The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.

E.The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.

Question 82

Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

A.Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.

B.Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.

C.Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.

D.Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.

E.Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.

Question 83

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 '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.

B.Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.

C.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.

D.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.

E.Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.

Question 84

You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

Question 85

You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A.Use compression='snappy', mode='overwrite')' and handle potential exceptions using a 'try-except' block.

B.Configure the stage 'my_stage' with FILE_FORMAT = (TYPE = 'JSON', COMPRESSION = 'SNAPPY') and then use within a 'try-except' block.

C.Use 'df.write.option('compression', inside a 'try-except block.

D.Define a UDF to write the dataframe into stage along with exception handling logic.

E.Wrap the entire write operation in a try-except block and implement retry logic with exponential backoff in case of transient errors.

Latest Upload: 206Cisco.300-610.v2026-08-19.q170; 194CompTIA.PT0-003.v2026-08-19.q179; 153Databricks.Databricks-Generative-AI-Engineer-Associate.v2026-08-18.q40; 148SAP.C_CR125_2601.v2026-08-17.q28; 210Salesforce.Field-Service-Consultant.v2026-08-17.q134; 167Oracle.1Z0-1170.v2026-08-16.q64; 175Oracle.1Z1-1170.v2026-08-16.q64; 200Juniper.JN0-336.v2026-08-13.q60; 373EMC.NCP-AII.v2026-08-13.q200; 222CIRO.RSE.v2026-08-12.q41

Question 81

Question 82

Question 83

Question 84

Question 85

Download PDF File