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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A) Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
B) Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
C) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
D) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
E) Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

2. You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

A)

B)

C)

D)

E)

3. You've created a Python stored procedure in Snowflake to train a model. The procedure successfully trains the model, saves it using 'joblib.dump' , and then attempts to upload the model file to an internal stage. However, the upload fails intermittently with a FileNotFoundErroN. The stage is correctly configured, and the stored procedure has the necessary privileges. Which of the following actions are MOST likely to resolve this issue? (Select TWO)

A) Before uploading the model to the stage, verify that the file exists using 'os.path.exists()' within the stored procedure. If the file does not exist, log an error and raise an exception.
B) Use the fully qualified path for the model file when calling 'joblib.dump'. E.g., 'joblib.dump(model, '/tmp/model.joblib')' instead of 'joblib.dump(model, 'model .joblib')'.
C) Before uploading the model to the stage, explicitly create the directory within the stage using 'snowflake.connector.connect()' and executing a 'CREATE DIRECTORY IF NOT EXISTS command on the stage. Then retry upload.
D) Implement error handling within the Python code to catch the 'FileNotFoundError' and retry the file upload after a short delay using 'time.sleep()'. The stored procedure should retry the upload a maximum of 3 times before failing.
E) Ensure that the Python packages used within the stored procedure (e.g., scikit-learn, joblib) are explicitly listed in the 'imports' clause of the 'CREATE PROCEDURE statement.

4. You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

A) Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
B) Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
C) Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.
D) Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
E) Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.

5. You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A) Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
B) Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
C) Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
D) Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
E) Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.

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