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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A)

B)

C)

D)

E)

2. You have a DataFrame 'df in Snowpark containing order data, including a VARIANT column named 'order details'. The 'order detailS column contains a nested JSON structure with fields like 'customer id' (always a string), 'items' (an array of item IDs, sometimes numbers, sometimes strings), and 'total amount' (inconsistent data type - sometimes string, sometimes number). You need to perform the following transformations: 1. Extract the "customer_id' and cast it to an integer. 2. Extract the first item ID from the 'items' array, attempting to cast it to an integer, handling potential casting errors. 3. Extract the 'total_amount' and cast it to a decimal (precision 10, scale 2), handling potential casting errors. Which of the following code snippets correctly implements these transformations using Snowpark?

A)

B)

C)

D)

E)

3. You are using Snowpark Python to build a data pipeline. You need to version control your Snowpark application and ensure that it is compatible with different Snowflake environments (development, staging, production). Which strategies and tools would be most effective for managing the Snowpark application's code, dependencies, and deployment process?

A) Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.
B) Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.
C) Use a Git repository to manage the Snowpark Python code, a dependency management tool like Poetry or pip to handle dependencies, and a CI/CD pipeline (e.g., using Jenkins or GitLab CI) to automate deployment to different Snowflake environments.
D) Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
E) Package all Snowpark code into a single ZIP file and manually upload it to each environment.

4. You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich it with data from a Snowflake table, and store the results in another Snowflake table. The enrichment process involves joining the streaming data with a large dimension table in Snowflake. Which of the following Snowpark features would be most efficient and scalable for this use case, considering the continuous nature of the streaming data and the size of the dimension table?

A) Creating a Snowflake materialized view that pre-joins the streaming data (landing in a table) with the dimension table and using Snowpark to query the materialized view.
B) Using a standard Snowpark DataFrame join operation with a broadcast hint on the smaller streaming data DataFrame.
C) Utilizing Snowpark UDFs to fetch data from the dimension table for each row of the streaming data DataFrame.
D) Employing a Snowpark Stored Procedure that periodically refreshes a smaller, aggregated version of the dimension table and using that for joining with the streaming data.
E) Leveraging a dynamic table to incrementally update the join of the streaming data and dimension table.

5. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
B) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
C) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
D) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
E) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.

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