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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineer is developing a Snowpark application using Python and needs to connect to Snowflake. They want to avoid hardcoding credentials directly in the script and utilize environment variables for authentication. Which of the following approaches is the MOST secure and RECOMMENDED way to retrieve Snowflake connection parameters (account, user, password, database, schema, warehouse, role) from environment variables and establish a Snowpark session?

A) Utilize the 'Session.builder.getorcreate()' method, assuming that Snowflake connection information (user, password, account, warehouse, etc.) are already set as environment variables with standard names, and let Snowpark automatically infer the parameters.
B) Store all connection parameters as a JSON string in a single environment variable and parse it within the Snowpark application.
C) Manually retrieve each parameter using 'os.environ.get()' and pass them directly into the 'Session.builder.configs()' method.
D) Use the SnowCLl to configure a connection profile, and then reference this profile name when creating the Snowpark session. Ensure each environment variable is also separately available, but are not explicitly called in the code to establish connection but only to set up SnowCLl.
E) Leverage the 'snowflake.connector.connect()' function with 'os.environ.get()' for credentials and then create a Snowpark session from the connection using Session. builder. from_connection(connectiony.

2. You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?

A) Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
B) Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.
C) Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.
D) Use the connection parameter along with username and password directly in the connection properties.
E) Pass username and password directly in the connection properties along with the account identifier.

3. You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?

A) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 'fillna()'. 3. Use the 'dayofweek' function to determine the day of the week and create using a 'when' condition.
B) 1. Calculate the mode of the 'transaction_date' column. 2. Fill the missing values in the 'transaction_date' column with the calculated mode. 3. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the 'is weekend transaction' column.
C) 1. Replace the null values in 'transaction_date' column with a constant string like '1900-01-01'.2. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 3. Apply the UDF to the 'transaction_dates column to create the column. 4. After applying the UDF convert back the replaced values in transaction_date to null.
D) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 3. Create a UDF using datetime library that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the column.
E) 1. Calculate the mode of the 'transaction_date' column. 2. Filter all rows where 'transaction_date' is null and load that data into a temporary table. 3. Update all rows in original 'transactions_df from temporary table. 4. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 5. Apply the UDF to the 'transaction_date' column to create the column.

4. You are developing a Snowpark application to process large datasets. You want to leverage asynchronous jobs to improve performance and prevent blocking the main thread. You have the following code snippet:

A) Use to retrieve the result with a timeout of 30 seconds, catching 'TimeoutError' if the job takes too long. This is the only way to get the result safely.
B) Continuously check 'job.status' in a loop until it returns 'SUCCESS', then retrieve the result using 'job.result()'. This is less efficient due to polling.
C) Call 'job.result(Y without any error handling. Snowflake will automatically handle any errors and return a null result.
D) Implement a callback function using 'job.on_success(callback_function)' and 'job.on_error(error_function)' to handle the result or any errors asynchronously.
E) Use 'job.getQueryld(Y to fetch the query ID and query the execution status directly from Snowflake using SQL. This is inefficient and bypasses the Snowpark API.

5. A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
B) Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.
C) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
D) Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
E) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.

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