Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 25)

Question 116

Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

A.The code is correct and will produce the desired output without any issues.

B.It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.

C.The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.

D.The code does not handle potential null values in the salary column. Consider using or before calculating the rank.

E.There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.

Question 117

You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A.Switch to a larger Snowflake warehouse size and increase the parameter to ' True'.

B.Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application.

C.Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures.

D.Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk.

E.Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.

Question 118

You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

Question 119

A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

A.Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.

B.Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.

C.Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.

D.Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)

E.Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.

Question 120

You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.