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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?

A)

B)

C)

D)

E)

2. When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

A) Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
B) Grant broad access to the environment variable containing the private key to all developers.
C) Regularly rotate the key pair.
D) Encrypt the private key at rest.
E) Store the private key directly in the Snowpark code repository.

3. You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A) Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
B) Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.
C) Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.
D) Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
E) Increase the warehouse size significantly to provide more resources for the scalar UDF execution.

4. You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.

A)

B)

C)

D)

E)

5. You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:

Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)

A)

B)

C)

D)

E)

Solutions: