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Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

A)

B)

C)

D)

E)

2. A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?

A) Build a custom Anaconda channel outside of Snowflake, and configure the Snowpark session to point to that channel.
B) Create a new Snowflake Anaconda environment and install pandas 1.1.5 into this environment, then configure the Snowpark session to use this custom environment. Ensure the 'snowflake-snowpark-python' package is also included.
C) Use 'conda install pandas==l .1.5' within the Snowpark Python code directly. This will install pandas at runtime.
D) Package the pandas 1.1.5 library within the Snowpark application code itself, by including it in the application deployment package.
E) Upload the pandas 1.1.5 wheel file directly into a Snowflake stage and reference it within the session configuration using along with

3. You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

A) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
B) Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.
C) Use 'CREATE OR REPLACE FUNCTION mydb.public.calculate_discount(order_total DOUBLE, customer_segment STRING, promo_code STRING) RETURNS DOUBLE LANGUAGE PYTHON ..: with the updated UDF definition.
D) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.
E) Use ALTER FUNCTION mydb.public.calculate_discount ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '

4. You are tasked with building a Snowpark application to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS'. The application should be deployed as a UDF. The sentiment analysis is performed by a third-party Python library, 'sentiment_analyzer'. Due to security constraints, direct internet access is prohibited from within the Snowflake environment. What steps are necessary to ensure the 'sentiment_analyzer' library can be used by your Snowpark UDF?

A) Package the 'sentiment_analyzer' library into a JAR file and upload it to a Snowflake stage, then specify the JAR in the 'imports' parameter of the UDF creation statement.
B) Package the 'sentiment_analyzer' library into a ZIP file and upload it to a Snowflake stage, then specify the ZIP in the 'imports' parameter of the UDF creation statement.
C) Request Snowflake support to whitelist the 'sentiment_analyzer' library for direct download during UDF execution.
D) Use the 'packages' parameter in the UDF creation statement to specify the 'sentiment_analyzer' library from Anaconda.
E) Install the 'sentiment_analyzer' library using 'conda install' directly within the Snowpark session before creating the UDF.

5. 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 A
B) Option B
C) Option D
D) Option C
E) Option E

Solutions: