You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

• A. Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.
• B. Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.
• C. Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
• D. Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
• E. Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.

You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

• A. Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.
• B. Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
• C. Drop one of the independent variable randomly if they seem highly correlated.
• D. Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.
• E. Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.

You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

• A. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
• B. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
• C. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
• D. Use the role within the pipeline script to create and grant all necessary privileges.
• E. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.

You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

• A. Option D
• B. Option E
• C. Option B
• D. Option A
• E. Option C

You have successfully trained a binary classification model using Snowpark ML and deployed it as a UDF in Snowflake. The UDF takes several input features and returns the predicted probability of the positive class. You need to continuously monitor the model's performance in production to detect potential data drift or concept drift. Which of the following methods and metrics, when used together, would provide the MOST comprehensive and reliable assessment of model performance and drift in a production environment? (Select TWO)

• A. Calculate the Kolmogorov-Smirnov (KS) statistic between the distribution of predicted probabilities in the training data and the production data over regular intervals. Track any substantial changes in the KS statistic.
• B. Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
• C. Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
• D. Continuously calculate and track performance metrics like AUC, precision, recall, and Fl-score on a representative sample of labeled production data over regular intervals. Compare these metrics to the model's performance on the holdout set during training.
• E. Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.