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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)

A)

B)

C)

D)

E)

2. 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 as a comment in the Store procedure code, and retrieve it using REGEX
D) Store the API key in a Snowflake table and query it within the stored procedure.
E) Store the API key as a constant string within the stored procedure's code.

3. You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

A)

B)

C)

D)

E)

4. You have developed a Snowpark Python application that needs to connect to an external REST API to enrich data during a transformation. The API requires authentication using an API key stored securely. Which of the following approaches is the MOST secure and recommended way to manage the API key within the Snowpark environment?

A) Store the API key in a secure vault outside of Snowflake and retrieve it using a custom Snowflake external function.
B) Hardcode the API key directly into the Snowpark Python code.
C) Store the API key in a Snowflake Secret Object and retrieve it within the Snowpark Python code using the function.
D) Encrypt the API key using a third-party encryption library and store it in a Snowflake table.
E) Store the API key as an environment variable within the Snowflake session.

5. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
B) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
C) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
D) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
E) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

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