Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are a data scientist working for a retail company that stores its transaction data in Snowflake. You need to perform feature engineering on customer purchase history data to build a customer churn prediction model. Which of the following approaches best combines Snowflake's capabilities with a machine learning framework (like scikit-learn) for efficient feature engineering? Assume your data is stored in a table named 'CUSTOMER TRANSACTIONS' with columns like 'CUSTOMER ID, 'TRANSACTION DATE, 'AMOUNT, and 'PRODUCT CATEGORY.

A) Load a small subset of 'CUSTOMER_TRANSACTIONS' into an in-memory database like Redis, perform feature engineering using custom Python scripts interacting with Redis, and periodically sync the results back to Snowflake.
B) Create a Snowflake external function that calls a cloud-based (AWS, Azure, GCP) machine learning service for feature engineering, passing the raw transaction data for each customer and processing the aggregated data into features in Snowflake SQL.
C) Extract all the data from 'CUSTOMER_TRANSACTIONS' into a Pandas DataFrame, perform feature engineering using Pandas and scikit-learn, and then load the processed data back into Snowflake.
D) Develop a custom Spark application to read data from Snowflake, perform feature engineering in Spark, and write the resulting features back to a new table in Snowflake, and avoid use of Snowflake SQL UDFs to minimize complexity.
E) Use Snowflake's SQL UDFs (User-Defined Functions) written in Python to perform feature engineering directly within Snowflake on smaller aggregated sets of data to optimize compute costs. Integrate these UDFs to query the entire 'CUSTOMER TRANSACTIONS table to build your features.

2. You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

A) Grant the 'USAGE privilege on the stage where the model files are stored to all users who need to deploy the model.
B) Grant the 'READ privilege on the container registry to all users who need to deploy the model. Create a custom role with the 'APPLY MASKING POLICY privilege and grant this role to the deployment team.
C) Create a custom role, grant the USAGE' privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry, and then grant this custom role to only those users authorized to deploy the model. Consider masking sensitive model parameters using masking policies.
D) Store the model outside of Snowflake managed registry and use external authentication to control access.
E) Grant the 'USAGE privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry itself, and grant the EXECUTE TASK' privilege to the deployment team for the deployment task.

3. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
B) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
D) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM

4. You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

A) Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
B) Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
C) Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.
D) Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
E) Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.

5. You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

A)

B)

C)

D)

E)

Solutions: