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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

A)

B)

C)

D)

E)

2. You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A) Install the library directly within the Snowflake Notebook using '!pip install
B) Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.
C) Create a deployment file using setup.py, upload deployment file to stage, and create function
D) Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
E) Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import

3. A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A) GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
B) GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
C) GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
D) GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
E) GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;

4. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key as an environment variable within the Snowflake warehouse configuration.
B) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
C) Store the API key in a Snowflake table and query it within the stored procedure.
D) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
E) Store the API key as a constant string within the stored procedure's code.

5. Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

A) There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.
B) The code is correct and will produce the desired output without any issues.
C) The code does not handle potential null values in the salary column. Consider using or before calculating the rank.
D) The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.
E) It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.

Solutions: