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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called 'CUSTOMER DATA. You want to assess the model's calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

A. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.
B. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.
C. Create a calibration curve (also known as a reliability diagram) by binning the predicted CLTV values, calculating the average predicted CLTV and the average actual CLTV within each bin, and plotting these averages against each other.
D. Calculate the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a hold-out test set to quantify the overall prediction accuracy.
E. Calculate the R-squared score on a hold-out test set to assess the proportion of variance in the actual CLTV explained by the model.

Answer: C

Explanation:
Option B is the most suitable technique for assessing calibration. A calibration curve directly visualizes the relationship between predicted and actual values, allowing you to see if the model is systematically over- or under-predicting CLTV for different ranges of predicted values. Options A, C, and D are useful for assessing overall accuracy and model fit but do not directly address calibration. MAE and RMSE (A) measure overall error magnitude. Residual analysis (C) can reveal problems with model assumptions. R-squared (D) measures the explained variance, not calibration. Option E measures whether two samples follow the same distribution, however, it would not be most suitable for assessing calibration of your CLTV regression model.

NEW QUESTION # 88
Consider the following Snowflake SQL query used to calculate the RMSE for a regression model's predictions, where 'actual_value' is the actual value and 'predicted value' is the model's prediction. However, you notice that the RMSE calculation is incorrect due to an error in the query. Identify the error in the query and provide the corrected query. The table name is 'sales_predictions'.

Which of the following options represents the corrected query that accurately calculates the RMSE?

Answer: D

Explanation:
The original query uses 'AVG', which is perfectly valid. However, Snowflake also supports the 'MEAN' function, which is an alias for the 'AVG' function and is used for calculated descriptive statistics. The corrected query maintains the functionality and structure, only substituting AVG with MEAN. B and C options lack the square root function. Option D calculates the Mean Absolute Error (MAE), not the RMSE. Option E uses a window function unnecessarily.

NEW QUESTION # 89
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

A. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
B. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
C. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

Answer: B

Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.

NEW QUESTION # 90
You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
B. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
C. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
D. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
E. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.

Answer: B,C,D,E

Explanation:
Option A is correct because is the standard method for accessing the active Snowpark session within a stored procedure. Option C is correct as the gsproc' decorator is required to register the function as a Snowpark Stored Procedure, specifying necessary packages. Option D correctly explains how to execute SQL queries using the session object and retrieve results. Option E accurately describes the function's ability to write a Pandas DataFrame to a Snowflake table and create it if it doesn't exist. Option B is incorrect because returns a Snowpark DataFrame, not a Pandas DataFrame. A Snowpark DataFrame is a lazily evaluated representation of the data, while a Pandas DataFrame is an in-memory copy.

NEW QUESTION # 91
You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?

A. A simple bar chart showing the overall accuracy score of the model alongside a table detailing the precision, recall, and F I-score. Include a word cloud of the most important features from the SHAP values.
B. A ROC curve (Receiver Operating Characteristic) showing the trade-off between true positive rate and false positive rate, paired with a detailed table of all feature importance scores generated by the SHAP explainer. Present statistical summaries, such as mean and standard deviation, of the top 5 feature values, grouped by predicted churn probability.
C. A confusion matrix visualizing the true positives, true negatives, false positives, and false negatives, along with a summary plot of the SHAP values showing the impact of each feature on the model's prediction for a representative sample of customers. A line chart showing cumulative churn rate across different customer segments.
D. A scatter plot showing the relationship between two key features identified by SHAP, colored by the model's churn prediction, and a table summarizing the model's performance metrics (accuracy, precision, recall, F I-score). Additionally, include a waterfall plot for a specific customer, illustrating how each feature contributes to the final prediction.
E. A distribution plot (e.g., histogram or KDE) of the predicted churn probabilities, segmented by actual churn status (churned vs. not churned), combined with a SHAP force plot visualizing the feature contributions for a single, randomly selected customer who churned. Add a section on potential cost savings from churn reduction.

Answer: C,D

Explanation:
Options B and D provide a balanced approach for both technical and non-technical audiences- A confusion matrix (Option B) is easily understandable and shows model performance across different prediction outcomes. A summary plot of SHAP values clearly illustrates feature importance and direction of impact. A line chart showing cumulative churn rate across different customer segments highlights the business value-Option D is also highly effective because scatter plots can be easily understood, especially when colored by churn prediction- The table of model metrics provides necessary details. The waterfall plot brings the explanation down to an individual customer level, making the model's behavior more tangible. Options A, C and E have deficits- Option A lacks detailed performance visualization. Option C is technical and might confuse non-technical stakeholders. Option E has too many summary plots.

NEW QUESTION # 92
......

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