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You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?
A. Option E
B. Option D
C. Option B
D. Option A
E. Option C
正解：A
解説：
Option E is the most correct. It includes the correct Snowflake UDF syntax, specifies the required packages (snowflake-snowpark- python, scikit-learn, pandas), imports the model from the stage, and defines a handler class with a 'predict' method that loads the model using pickle and performs the prediction. It also correctly utilizes the to access files from the stage. Other options have errors in syntax, file access within the UDF environment or how input features are handled.
質問 # 240
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. Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
B. 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.
C. 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.
D. Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
E. Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.
正解：B、C
解説：
Options B and D provide the most comprehensive assessment of model performance and drift. Option D, by continuously calculating key performance metrics (AUC, precision, recall, F1 -score) on labeled production data, directly assesses how well the model is performing on real- world data. Comparing these metrics to the holdout set provides insights into potential overfitting or degradation over time (concept drift). Option B, calculating the KS statistic between the predicted probability distributions of training and production data, helps to identify data drift, indicating that the input data distribution has changed. Option A can be an indicator but is less reliable than the KS statistic. Option C monitors data pipeline health, not model performance. Option E focuses on data quality, which is important but doesn't directly assess model performance drift.
質問 # 241
You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?
A. Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
B. Using a simple moving average to smooth the data before applying a linear regression model.
C. Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
D. Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).
E. Applying exponential smoothing directly to the original time series without feature engineering.
正解：C、D
解説：
Both creating lagged features (Option C) and using Fourier terms (Option E) are effective approaches for capturing seasonality in a supervised learning framework. Lagged features directly encode the past values of the time series, capturing the relationships and dependencies within the data. This is particularly effective when there are strong autocorrelations. Fourier terms represent periodic patterns in the data using sine and cosine waves. By including Fourier terms with frequencies corresponding to daily and weekly cycles, the model can learn to capture the seasonal variations in energy consumption. Option A is too simplistic and doesn't capture the nuances of seasonality. Option B, while valid, might be more complex to implement and maintain than Option C and E. Option D is generally less accurate than the feature engineering approaches.
質問 # 242
You have successfully deployed a machine learning model in Snowflake using Snowpark and are generating predictions. You need to implement a robust error handling mechanism to ensure that if the model encounters an issue during prediction (e.g., missing feature, invalid data type), the process doesn't halt and the errors are logged appropriately. You are using a User-Defined Function (UDF) to call the model. Which of the following strategies, when used IN COMBINATION, provides the BEST error handling and monitoring capabilities in this scenario?
A. Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
B. Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
C. Implement a custom logging solution by writing error messages to an external file storage (e.g., AWS S3) using an external function called from within the UDE
D. Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.
E. Use a 'TRY...CATCH' block within the UDF to catch exceptions, log the errors to a separate Snowflake table, and return a default prediction value (e.g., NULL) for the affected row.
正解：B、E
解説：
The combination of A and D provides the best error handling and monitoring. A 'TRY...CATCH' block within the UDF allows for graceful handling of exceptions and prevents the entire process from failing. Logging errors to a separate Snowflake table allows for easy analysis and debugging. Returning a default value ensures that downstream applications don't encounter unexpected errors due to missing predictions. Snowflake's event tables capture a broader range of errors and audit logs, providing a comprehensive view of the UDF's execution. Option B is insufficient as it relies solely on post-mortem analysis. Option C is useful for performance profiling but doesn't address error handling directly. Option E introduces external dependencies and complexity when a native Snowflake solution is available and potentially introduces latency in the prediction process. It also can impact costs since you are using external function to copy the logs outside snowflake, where cost will be charged.
質問 # 243
A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?
A. Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
B. Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.
C. Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
D. Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.
E. Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.
正解：A
解説：
Option E provides the most efficient and appropriate solution. It uses SQL to aggregate the data by day using DATE TRUNC and calculates the total sales amount, addressing the data preparation part. Snowsight can then be used to generate a line chart, making it easy to visualize the trend over time. The usage of moving average via window functions add a layer to smooth the data so that the outliers can be removed. Other options are less efficient (exporting data to external tools) or don't directly address the visualization of trends (showing raw data in a table or manually filtering data).
