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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

A)

B)

C)

D)

E)

2. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
B) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
C) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
D) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
E) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.

3. You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.
B) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
C) Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional
D) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
E) Configure the Snowpark session with the parameter set to ' FALSE

4. 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) Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
B) 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.
C) Package all Snowpark code into a single ZIP file and manually upload it to each environment.
D) Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.
E) Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.

5. You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A) The Anaconda environment is not activated. Activate the environment using 'conda activate
B) The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'
C) The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
D) The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
E) The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.

Solutions: