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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A) Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
B) Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
C) Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
D) Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.
E) Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.

2. You have a Snowpark DataFrame named containing order data that needs to be inserted into the 'ORDERS table. However, due to a recent data ingestion issue, some records in might already exist in the 'ORDERS table based on the 'ORDER ID' column. Your goal is to insert only the new orders into the 'ORDERS table while avoiding duplicates. Which of the following approaches, combining efficiency and correctness, is most suitable for this task? Assume 'session' and required libraries are already imported.

A) Option C
B) Option A
C) Option D
D) Option B
E) Option E

3. A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
C) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,
D) GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
E) GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;

4. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
B) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.
C) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.
D) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
E) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.

5. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
B) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
C) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.
D) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
E) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.

Solutions: