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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame representing sensor data. The DataFrame contains columns like 'timestamp', 'sensor id' , and 'value'. You need to perform a complex windowing operation to calculate the moving average of the 'value' for each 'sensor id' over a 5-minute window, but only for data points where the 'value' is greater than a threshold. The window should be defined based on the 'timestamp' column. What is the most efficient and correct approach to implement this using Snowpark DataFrames?

A) Create a UDF that takes a list of timestamps and values as input and returns the moving average. Apply this UDF to the entire DataFrame.
B) First apply the moving average calculation to the DataFrame and then filter for rows with values exceeding the threshold, since calculations are performed in order.
C) Use a loop to iterate over each 'sensor_id' , filter the DataFrame for that sensor, calculate the moving average using Pandas windowing functions, and then combine the results.
D) First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
E) Use a combination of 'filter' to apply the threshold condition, 'Window.partitionBy' and 'Window.orderBy' to define the window, and 'avg' window function to calculate the moving average.

2. You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?

A)

B)

C)

D)

E)

3. A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A) Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.
B) Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.
C) Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.
D) Use to explicitly set connection parameters instead of relying on environment variables for debugging.
E) Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'

4. Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'sales df that contains daily sales data'. You need to calculate the weekly sales for each product and store the results in a new DataFrame. The calculation of weekly sales involves a window function that is computationally expensive. To optimize performance, you decide to cache the DataFrame after applying the window function. However, after implementing the caching, you notice that the performance is not improved as expected. What could be the reason for this and how can you fix it?

A) The call is placed before the window function. Move the call after applying the window function.
B) The DataFrame is being evicted from the cache due to memory pressure. Increase the warehouse size or reduce the data being processed.
C) The DataFrame is too small. Caching only benefits large DataFrames.
D) Snowflake automatically optimizes window function calculations, rendering explicit caching unnecessary.
E) The window function is not cacheable. Window functions cannot be cached using 'cache_result()'.

Solutions: