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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
B) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
C) Create an external function and grant access to the API integration that provides the security context.
D) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
E) Create a UDF and grant USAGE on the stage to the role that owns the UDF.

2. You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called 'RAW SENSOR DATA'. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named 'ENRICH SENSOR DATA, is written in Python and resides in a stage called 'UDF STAGE. The UDF takes three arguments: 'timestamp', and 'raw_value', all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

A)

B)

C)

D)

E)

3. A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.

A)

B)

C)

D)

E)

4. A Snowpark Python application is failing intermittently with a 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: Remote service internal error [Errorld: ...l' when calling 'df.collect()' on a DataFrame that results from joining multiple tables and applying a complex filter. The data volume is substantial, but within the warehouse's expected capacity. Which of the following actions are MOST likely to resolve this issue? (Select two)

A) Increase the parameter to a higher value to prevent session timeouts.
B) Implement retry logic around the 'df.collect()' call with exponential backoff, assuming the error is transient due to resource contention.
C) Switch to using the function with a raw SQL query instead of Snowpark DataFrame operations.
D) Break down the complex query into smaller, intermediate DataFrames and persist them using to avoid memory pressure during a single large query.
E) Replace with 'df.toPandas(Y to improve memory management on the client side.

5. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
B) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
C) To improve the performance of the stored procedure by enabling compile-time optimizations.
D) To enable the stored procedure to be called from other programming languages besides Python.
E) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.

Solutions: