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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

2. Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A) Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
B) Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
C) Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.
D) Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
E) Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.

3. You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A) Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
B) Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
C) Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
D) Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
E) Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.

4. You are evaluating a binary classification model built in Snowflake for predicting customer churn. You have access to the model's predictions on a holdout dataset, and you want to use both the ROC curve and the confusion matrix to comprehensively assess its performance. Which of the following statements regarding the interpretation and use of ROC curves and confusion matrices are correct in this scenario?

A) In Snowflake, you can generate ROC curves and confusion matrices directly using the 'SYSTEM$PREDICT function with appropriate parameters and visualizing the results using a tool like Snowsight or Tableau.
B) The ROC curve visualizes the trade-off between true positive rate (sensitivity) and false negative rate (1 - specificity) at various threshold settings.
C) The area under the ROC curve (AUC) provides a single scalar value representing the overall discriminatory power of the model, with a higher AUC indicating better performance. An AUC of 0.5 indicates that the model performs no better than random chance.
D) The confusion matrix allows you to calculate precision, recall, F I-score, and accuracy, which are all useful for understanding the model's performance in terms of correctly and incorrectly classified instances.
E) While the ROC curve is independent of the class distribution, the metrics derived from the confusion matrix (e.g., precision, recall) can be significantly affected by imbalanced datasets.

5. You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

A) Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.
B) Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
C) Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.
D) Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
E) Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.

Solutions: