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最新的 SnowPro Advanced DSA-C03 免費考試真題:

1. You've built a regression model in Snowflake to predict customer churn. You've calculated the R-squared score on your test data and found it to be 0.65. However, after deploying the model to production and monitoring its performance over several weeks, you notice the model's predictive accuracy has significantly decreased. Which of the following factors could contribute to this performance degradation?
Select all that apply.

A) Feature engineering inconsistencies: The feature engineering steps applied to the production data are different from those applied during training.
B) Data drift: The distribution of the input features in the production data has changed significantly compared to the training data.
C) Increased data volume: The production data volume has increased significantly, causing resource contention and impacting model performance in Snowflake.
D) Overfitting: The model learned the training data too well, capturing noise and specific patterns that do not generalize to new data.
E) Bias Variance trade off : Model is having high bias.

2. You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

A) Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.

B) The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
C) Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
D) Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.

E) The best way to calculate AUC is to randomly guess the probabilities and see how it performs.

3. You are deploying a pre-trained image classification model stored as a serialized file in an internal stage within Snowflake. You need to create a UDF to load this model and use it for inference on image data stored in a VARIANT column. The model was trained using Python's scikit-learn library and uses OpenCV for image processing. Which of the following code snippets correctly outlines the steps required to create and deploy this UDF? Assume you have already created an internal stage named 'MODEL STAGE and uploaded the model file into it. You also need to create a temporary directory that will be removed after the execution.

A)

B)

C)

D)

E)

4. You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A) Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
B) Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
C) Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
D) Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
E) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.

5. You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

A) Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
B) Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
C) Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
D) Increase the size of the training dataset significantly by querying data from external sources.
E) Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.

問題與答案：