Snowflake SPS-C01

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that performs several data transformations. You need to implement error handling to catch exceptions during DataFrame operations and log them to a Snowflake table named 'error _ log'. You also need to ensure that if any exception occurs, the application continues to run and processes the remaining data. Which of the following approaches (or combination of approaches) would be MOST suitable?

A) Rely solely on Snowflake's built-in error handling mechanisms, as Snowpark automatically handles all exceptions.
B) Wrap individual DataFrame operations (e.g., 'filter', 'select, 'groupBy') in try-except blocks to catch exceptions specific to those operations and log them.
C) Implement a custom User-Defined Function (UDF) that encapsulates the error-prone logic. Within the UDF, use try-except blocks to handle errors and return error messages along with the processed data. Add a column in Dataframe that contains the errors. Then, create table for the Dataframe using snowpark's API
D) Use Snowpark's logging API to automatically capture and log all errors without needing explicit try-except blocks. (Assume such an API exists, even if it doesn't in the current Snowpark version.)
E) Use a global try-except block to catch all exceptions and log them to the 'error_log' table.

2. A data engineering team is developing a Snowpark stored procedure in Python to perform anomaly detection on time-series data stored in a Snowflake table named 'sensor_readingS. The stored procedure needs to efficiently process large volumes of data and return only the rows identified as anomalies. Which of the following approaches would provide the most performant and scalable solution for operationalizing this stored procedure?

A) Load the entire 'sensor_readings' table into a Pandas DataFrame within the stored procedure, perform anomaly detection using a Python library like 'scikit-learn' , and then create a Snowpark DataFrame from the filtered Pandas DataFrame to return the results.
B) Use the Snowpark API to directly perform anomaly detection calculations (e.g., rolling statistics, z-score calculations) on the 'sensor_readings' table within the stored procedure, leveraging Snowpark's distributed processing capabilities, and then return the resulting Snowpark DataFrame containing only the anomalies.
C) Execute a SQL query from within the stored procedure using the Snowflake connector for Python to fetch the relevant data, then use a standard Python loop to iterate through the results and apply anomaly detection logic. Return the anomalous rows as a list of dictionaries.
D) Create a UDF with a Scala implementation and use it inside the Snowpark stored procedure to detect anomalies using the Scala implementation for increased processing power.
E) Use the method to include a pre-trained anomaly detection model (pickled object) in the stored procedure's execution environment. Load the model, use it to predict on the data fetched using 'session.table(Y , and return a Snowpark DataFrame of anomalies.

3. Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

A) Using username/password authentication is the most secure and recommended approach for production environments.
B) Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.
C) OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
D) OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
E) Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.

4. Consider the following Snowpark Python code snippet that defines and registers a User-Defined Table Function (UDTF):

Which of the following statements is MOST accurate regarding the behavior and limitations of this UDTF when used in a Snowpark DataFrame transformation?

A) If the input DataFrame column contains NULL values, the 'process' method will receive 'None' as the value for 'input_string'. The 'output_schema' correctly defines the structure of the output rows.
B) The UDTF can only be used with DataFrames that have been explicitly persisted as Snowflake tables.
C) The UDTF will process each input string in parallel, with Snowflake automatically distributing the processing across multiple worker nodes.
D) The UDTF will be executed within the same Python process as the Snowpark driver program, limiting its scalability for large datasets.
E) The 'input_string' argument passed to the 'process' method will always be a single string value, even if the input DataFrame column contains NULL values.

5. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

Solutions: