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Question 1

A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A. Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
B. Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.
C. Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.
D. Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.
E. Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.

Question 2

You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?

A. Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
B. Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
C. Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
D. Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
E. Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.

Question 3

You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

Question 4

You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?

A. Configure Snowflake's network policies to allow outbound network access from the UDTF to the specific REST API endpoint. This will eliminate the network connectivity issues and prevent the UDTF from failing.
B. Use a combination of retry mechanisms (like the tenacity library) with exponential backoff around the API call. If the retry fails after a predefined number of attempts, then return pre-computed data or use a simplified model as the UDTF's output.
C. Use the 'try...except' block specifically around the code that makes the API call. Within the 'except block, catch specific network-related exceptions (e.g., requests.exceptions.RequestException', 'socket.timeout'). Log the error to a Snowflake stage using the 'logging' module and retry the API call a limited number of times with exponential backoff.
D. Implement a global exception handler within the UDTF that catches all exceptions, logs the error message to a Snowflake table, and returns a default forecast value when a network error occurs. Ensure the error logging table exists and has sufficient write permissions for the UDTF.
E. Before making the API call, check the network connectivity using the 'ping' command. If the ping fails, skip the API call and return a default forecast value. This prevents the UDTF from attempting to connect to an unavailable endpoint.

Question 5

A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

A. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
B. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
C. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
D. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.
E. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.

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