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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

A)

B)

C)

D)

E)

2. Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

A) The 'return df.collect()' line must be replaced with 'return and the 'return_type' and 'input_typeS arguments of udf must be removed to allow inference. The rest of the code remains unchanged.
B) The 'return df.collect()' line must be replaced with 'return and 'return_type' and 'input_typeS arguments of udf must be removed. The rest of the code remains unchanged.
C) The 'return df.collect()' line must be replaced with 'return and the Snowflake session object must be explicitly passed to the UDF when it is called.
D) No changes are required; the code will function as a stored procedure as is.
E) The 'return df.collect()' line must be replaced with 'return df and create a DataFrame. The rest of the code remains unchanged.

3. You are working with a Snowpark DataFrame 'products df' containing product information, including 'product_id', 'price', and 'discount'. You need to update the 'price' column in the 'products' table based on the following logic: If 'discount' is greater than 0.2, reduce the 'price' by 15%. If 'discount' is between 0.1 and 0.2 (inclusive), reduce the 'price' by 5%. Otherwise, keep the 'price' as is. Which of the following Snowpark code snippets efficiently implements this update? Assume 'products' table already exists and is correctly populated.

A) Option B
B) Option E
C) Option C
D) Option D
E) Option A

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

5. You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A) Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.
B) Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
C) Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
D) Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
E) Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.

Solutions: