User-defined functions with Snowpark in Snowflake - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

What is the output of this Snowpark UDF execution?

Given a Snowflake Snowpark UDF that doubles an integer input, what will be the output of calling it with input 5?

Snowflake

from snowflake.snowpark.functions import udf

@udf
def double_value(x: int) -> int:
    return x * 2

result = double_value(5).collect()[0][0]

A 25

B 5

C 10

D None

💡 Hint

Think about what doubling 5 means mathematically.

The UDF multiplies the input by 2, so 5 * 2 = 10.

❓ Configuration

intermediate

Which option correctly registers a Python UDF in Snowflake using Snowpark?

Select the code snippet that correctly registers a Python UDF named add_one that adds 1 to an integer input.

A session.udf.add('add_one', lambda x: x + 1, IntegerType())

B session.udf.register(lambda x: x + 1, return_type=IntegerType(), input_types=[IntegerType()], name='add_one')

C session.create_udf('add_one', lambda x: x + 1, IntegerType())

D session.udf.create('add_one', lambda x: x + 1, IntegerType())

💡 Hint

Look for the method that registers a UDF with name, input, and return types.

The session.udf.register method is the correct way to register a Python UDF with specified input and return types.

❓ Architecture

advanced

Which Snowpark UDF design best supports processing large datasets efficiently?

Choose the UDF design that optimizes performance when applied to large Snowflake tables.

A A vectorized UDF that processes batches of rows in parallel

B A scalar UDF that processes one row at a time without vectorization

C A UDF that writes intermediate results to external storage during execution

D A UDF that uses global variables to store state across calls

💡 Hint

Think about how batch processing can improve speed over row-by-row processing.

Vectorized UDFs process multiple rows at once, reducing overhead and improving performance on large datasets.

❓ security

advanced

What is the best practice to secure Snowpark UDFs that access sensitive data?

Which option ensures that a Snowpark UDF accessing sensitive data follows security best practices?

A Grant the UDF owner minimal privileges and use role-based access control

B Embed credentials inside the UDF code for direct database access

C Allow public access to the UDF for easier sharing

D Disable all logging to hide UDF activity

💡 Hint

Consider how to limit access and track usage securely.

Using minimal privileges and role-based access control limits exposure and follows the principle of least privilege.

🧠 Conceptual

expert

What error will this Snowpark UDF raise when called with a string input?

Consider this UDF that expects an integer input but is called with a string:

from snowflake.snowpark.functions import udf

@udf
def square(x: int) -> int:
    return x * x

result = square('text').collect()[0][0]

What error occurs?

A No error, returns 'texttext'

B ValueError: invalid literal for int() with base 10: 'text'

C SnowparkUDFExecutionError: Input type mismatch

D TypeError: unsupported operand type(s) for *: 'str' and 'str'

💡 Hint

Think about what happens when multiplying a string by a string in Python.

Multiplying a string by a string is not supported in Python, causing a TypeError.