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| Section | Weight | Objectives |
|---|---|---|
| Data Transformations and Operations | 35% | - User-defined logic
|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Snowpark API and Development | 30% | - Multi-language support
|
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NEW QUESTION # 119
Consider the following Snowpark code snippet that defines and registers a UDF:
Which of the following statements about this code are TRUE?
Answer: A,D,E
Explanation:
The correct answers are C, D, and E. makes the UDF permanent. 'replace=True' overwrites any existing UDF with the same name. Python's default parameter value IS used in the SQL call if the salutation is omitted. 'input_typeS are not redundant, they are required and Python's type hints are not automatically used. Option A is incorrect because 'is_permanent' is set to true.
NEW QUESTION # 120
You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?
Answer: B
Explanation:
Option D correctly uses the 'is_number' function to check if the timestamp is numeric. If it is, it divides by 1000 (assuming milliseconds) and converts to a timestamp. If it's not numeric, it converts directly to a timestamp (assuming it is a string representation). Options A, B, C, and E will fail when encountering mixed data types because 'to_timestamp' expects either a number or a string, not both interchangeably. Casting numeric value as String then passing to 'to_timestamp' would raise issues, it needs to be divided.
NEW QUESTION # 121
You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.
Answer: A,C
Explanation:
Including the library's code directly into the UDF is not manageable and maintainable. Snowflake's Anaconda channel simplifies dependency management by providing a curated set of packages. Snowflake's managed dependencies and secure execution environment ensure that the UDF runs in a secure and isolated environment. Disabling security is unacceptable. While external functions could manage dependencies, using the built-in Anaconda integration is simpler within Snowflake. It's also more performant than the overhead of an external function call.
NEW QUESTION # 122
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?
Answer: A,B
Explanation:
Options B and E are correct. Option B correctly registers the function 'select_columnS as a stored procedure using "session.sproc.register'. Option E properly constructs the DataFrame by dynamically selecting columns by using 'df[col]'. Option A although syntactically correct may not perform as expected. Option C is incorrect because it attempts to use 'ArrayType' for a standard Python List, which is incompatible. Option D uses columns: str' which makes column as Tuple object instead of List object.
NEW QUESTION # 123
You have written a Snowpark Python function that utilizes a UDF to perform complex string manipulation on a DataFrame containing customer reviews. When deploying this function using '@sproc.test_utils.mock_snowflake environment, the test fails with a 'ModuleNotFoundError' indicating that a custom Python library (e.g., is not available. You have already confirmed that the library is installed in your local development environment. What is the MOST reliable way to ensure the UDF has access to this dependency during local testing?
Answer: B
Explanation:
Option C is the most reliable solution for local testing with . The function explicitly makes the library available to the Snowpark session during execution, simulating how dependencies are handled in the Snowflake environment. Option A might work locally, but it's not a reliable deployment strategy. Option B is a temporary workaround and not a structured solution. Option D makes the library globally available, defeating the purpose of isolating dependencies for testing. Option E is a valid approach but less explicit and may affect other Python environments on the system. Using 'add_import' ensures that the correct version and dependencies are included in the deployment package.
NEW QUESTION # 124
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