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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame 'df_orders' containing order data'. You want to delete all records from the underlying Snowflake table 'ORDERS TABLE' where the 'order_date' is older than '2023-01-01' using a Snowpark DataFrame operation. Which of the following code snippets is the MOST efficient and recommended way to achieve this, assuming 'spark' is your Snowpark Session object?

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

2. You have a Snowflake stage containing image files. You need to write a Snowpark Python application that extracts metadata (e.g., image resolution, format) from these images and stores the metadata in a Snowflake table. You want to leverage a Python library, such as Pillow (PIL), for image processing. Which of the following steps are necessary to correctly and efficiently implement this?

A) Create a Python UDF (User-Defined Function) that uses Pillow to extract metadata from the image files. Register the UDF with Snowflake. In a Snowpark DataFrame transformation, call the UDF for each image file to extract the metadata. Finally, write the resulting DataFrame to a Snowflake table.
B) Use Snowpark's built-in image processing functions to extract metadata directly from the image files. This eliminates the need for external libraries like Pillow.
C) Upload the Pillow library as a zip file to a Snowflake internal stage. Create a Snowpark stored procedure. In the stored procedure code, import the Pillow library using 'import zipfile; sys.path.append('pillow.zip'); from PIL import Image'. Read the image files using , process them with Pillow to extract metadata, and then insert the metadata into the Snowflake table.
D) Create a Conda environment specification file ('environment.yml') that includes Pillow as a dependency. Upload the 'environment.ymr file to a Snowflake stage. Use 'session.add_packages' in the Snowpark session to load the Pillow library. Read the image files using , process them with Pillow, and then write the metadata to a Snowflake table using 'session.write_pandas()'.
E) Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session

3. You are developing a Snowpark Python application to process large datasets stored in a Snowflake table called 'CUSTOMER DATA' The application needs to perform complex data transformations and aggregations that benefit from Snowpark's lazy evaluation and query optimization. Which of the following approaches will lead to the MOST efficient execution in terms of resource utilization and performance?

A) Create a Snowpark DataFrame from the "CUSTOMER_DATX table using 'session.table('CUSTOMER DATA')', perform all the transformations and aggregations using Snowpark DataFrame API, and materialize the final result into a new Snowflake table using .
B) Load the data into a temporary table, then create a Stored Procedure in SQL to perform transformations, and call the procedure from the Snowpark Python application.
C) Fetch all the data from 'CUSTOMER_DATR into a Pandas DataFrame using , perform the transformations in Pandas, and then load the results back into a new Snowflake table using 'session.write_pandas()'.
D) Execute a series of individual SQL queries using 'session.sql()' to perform the transformations and aggregations, and then combine the results into a final Pandas DataFrame for further processing before loading it back into Snowflake.
E) Use 'session.table('CUSTOMER to fetch all the data into a list of Row objects in Python, perform transformations using standard Python loops and data structures, and then write the processed data to a new Snowflake table using 'session.createDataFrame(V.

4. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

5. A Snowpark application needs to authenticate to Snowflake using OAuth. The application is running on an Azure Function and uses a client ID, client secret, and refresh token obtained previously. Which of the following connection parameter dictionaries is correctly configured for OAuth authentication?

A)

B)

C)

D)

E)

Solutions: