Snowflake : SPS-C01 Questions & Answers

Which of the following are key benefits of using Snowpark's server-side execution capabilities for data processing tasks within Snowflake, compared to performing the same tasks on the client-side? (Select all that apply)

• A. Improved security by keeping data within Snowflake's secure environment.
• B. Greater flexibility in choosing programming languages for data processing.
• C. Reduced data transfer costs between the client and Snowflake.
• D. Simplified code deployment and maintenance.
• E. Increased processing speed due to leveraging Snowflake's scalable compute resources.

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A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

• A. Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.
• B. Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
• C. Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
• D. Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.
• E. Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.

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You have a Snowpark DataFrame 'customer data df containing customer information, including 'customer id', 'email', and phone number'. You need to anonymize the 'email' and 'phone number" columns for customers residing in specific countries (e.g., 'USA', 'Canada') before persisting the changes back to the 'customers' table. Anonymization should replace sensitive data with 'XXXXX'. You want to leverage UDF for obfuscation. What is correct and optimal approach considering performance and security?

• A. Option E
• B. Option B
• C. Option D
• D. Option A
• E. Option C

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

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You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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