Snowflake Snowflake Certification SPS-C01

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

A) Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
B) Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.
C) Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.
D) Reduce the 'window_size' parameter. Smaller windows inherently require less computation.
E) Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.

2. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:

A)

B)

C)

D)

E)

4. You are working with a Snowpark DataFrame named 'sales df' containing sales data including columns 'product id', 'sale date', and 'sale_amount'. You want to create a new DataFrame 'filtered df that only includes rows where the 'product id' is present in a list of approved product IDs and the 'sale_amount' is greater than the average sale amount. You have already calculated the average sale amount and stored it in a variable named 'avg_sale amount'. Which of the following code snippets correctly achieves this?

A)

B)

C)

D)

E)

5. You are working with a Snowpark 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 all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?

A)

B)

C)

D)

E)

Solutions: