Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 27)

Question 126

You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

A.Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.

B.Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.

C.Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.

D.Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.

E.Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.

Question 127

You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

Question 128

You have a Snowpark application that processes sensitive data'. To enhance security, you want to use key pair authentication and ensure that the private key is never exposed in plain text within the application or logs. Which of the following strategies offers the most robust protection against accidental key exposure, even in the event of a security breach of the application server itself?

A.Store the private key in an environment variable on the application server, ensuring that only the application user has read access.

B.Store the private key encrypted with a symmetric key. Store the symmetric key in a separate environment variable.

C.Use a dedicated Hardware Security Module (HSM) or a cloud-based key management service (e.g., AWS KMS, Azure Key Vault) to store and manage the private key. Configure the Snowpark application to retrieve the key only when needed and never store it locally.

D.Store the private key encrypted using AES encryption with a key derived from a passphrase. Prompt the user for the passphrase each time the application starts.

E.Store the private key in an encrypted file with restricted access, decrypting it only during session creation and overwriting the memory location immediately afterwards.

Question 129

You are developing a Snowpark application in Python to process financial transactions. You're using a Snowpark DataFrame named 'transactions_df which contains sensitive financial data, including customer account numbers and transaction amounts. To comply with data privacy regulations, you need to mask the customer account numbers before performing any aggregations. The 'transactions_df DataFrame has a column named 'ACCOUNT NUMBER' (VARCHAR). You decide to use a User-Defined Function (UDF) to mask the account numbers using a cryptographic hashing algorithm. Which of the following approaches is the most secure and efficient way to define and use the UDF in Snowpark, ensuring the masking occurs within the Snowflake environment and minimizes the risk of exposing sensitive data?

A.Create a Python UDF that retrieves the account numbers, performs the masking operation using Python's 'hashlib' library, and returns the masked account numbers as a new DataFrame. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()&.

B.Create a Java UDF that uses a strong cryptographic hashing algorithm provided by the Java Cryptography Extension (JCE) to mask the account numbers. Deploy this UDF to Snowflake and apply it to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'. Ensure the Java UDF has necessary dependencies available.

C.Create a SQL UDF that uses Snowflake's built-in ' SHA2 function to mask the account numbers. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'.

D.Create a Python UDF that uses a simple string manipulation technique (e.g., replacing characters with asterisks) to mask the account numbers. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'.

E.Extract the 'ACCOUNT NUMBER column from the 'transactions_df DataFrame, iterate through the account numbers in your Python application, mask them using a strong cryptographic algorithm, and then create a new Snowpark DataFrame with the masked account numbers.

Question 130

You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?