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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A) Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.
B) Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.
C) Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .
D) Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.
E) Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.

2. You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.

A) Use Snowflake's Anaconda channel to manage dependencies and specify the required packages in the UDF's registration using 'packages' argument.
B) Utilize Snowflake's managed dependencies to handle packages installation and versioning automatically. Register the UDF to run in a secure execution environment within Snowflake.
C) Deploy the UDF as an external function and manage dependencies using the external function's environment.
D) Include the third-party library's code directly within the UDF's code.
E) Disable security features to avoid dependency conflicts.

3. You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

A)

B)

C)

D)

E)

4. You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

A) Convert 'data_df to a Pandas DataFrame using and then use to insert the data.
B) Directly use the method, without any intermediate transformations.
C) Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively
D) Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.
E) Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.

5. You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A) Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.
B) Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =
C) Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).
D) Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.
E) Using the method to replace null values in the 'description' column with a default string 'No description available'.

Solutions: