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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q258-Q263):

NEW QUESTION # 258
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. 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.
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. 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.
E. 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.

Answer: C,E

Explanation:
Options B and E are the MOST appropriate for handling network errors. Using a 'try...except block (B) specifically targets the API call and allows for handling network-related exceptions gracefully. Logging the error to a Snowflake stage provides valuable debugging information. Retry with exponential backoff increases the chances of success during transient network issues. Option E improves upon option B with external and maintained libraries such as tenacity and returning a model output, not just a single value, when the error is recoverable or the data is missing. Option A, a global exception handler, is too broad and might mask other errors. Option C is a necessary prerequisite but does not address intermittent connectivity issues. Option D's 'ping' command is not reliable for determining API availability and might introduce unnecessary delays or false negatives. A complete end-to-end, complete solution must focus on addressing all aspects of code and execution.

NEW QUESTION # 259
A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A. Calculated as 'applicant_age applicant_age'.
B. Calculated as 'applicant_income I loan_amount'.
C. Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
D. Calculated as 'applicant_age / applicant_income'.
E. Calculated as 'loan_amount I applicant_age' .

Answer: B,C,E

Explanation:
The best combination provides diverse perspectives on financial stability. directly reflects the applicant's ability to cover the loan with their income. represents the loan burden relative to the applicant's age and can expose risk in younger, less established applicants. provides the most comprehensive view, including existing debt obligations from external data. "age_squared' and are less directly informative about repayment ability. They could potentially capture non-linear relationships, but 'age_squareff is more likely to introduce overfitting. relies on an external data source, making it a powerful, but potentially more complex, feature to implement.

NEW QUESTION # 260
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 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.
B. 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.
C. 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.
D. 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.
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: E

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

NEW QUESTION # 261
You have trained a classification model in Snowflake using Snowpark ML to predict customer churn. After deploying the model, you observe that the model performs well on the training data but poorly on new, unseen data'. You suspect overfitting. Which of the following strategies can be applied within Snowflake to detect and mitigate overfitting during model validation , considering the model is already deployed and receiving inference requests through a Snowflake UDF?

A. Create shadow UDFs that score data using alternative models. Compare the performance metrics (such as accuracy, precision, recall) between the production UDF and shadow UDFs using Snowflake's query capabilities. If shadow models consistently outperform the production model on certain data segments, retrain the production model incorporating those data segments with higher weights.
B. Since the model is already deployed, the only option is to collect inference requests and compare the distributions of predicted values in each batch with the predicted values on the training set. A large difference indicates overfitting; model must be retrained outside of the validation process.
C. Monitor the UDF execution time in Snowflake. A sudden increase in execution time indicates overfitting. Use the 'EXPLAIN' command on the UDF's underlying SQL query to identify performance bottlenecks and rewrite the query for optimization.
D. Implement k-fold cross-validation within the Snowpark ML training pipeline using Snowflake's distributed compute. Track the mean and standard deviation of the performance metrics (e.g., accuracy, Fl-score) across folds. A high variance suggests overfitting. Use this information to tune hyperparameters or select a simpler model architecture before deployment.
E. Calculate the Area Under the Precision-Recall Curve (AUPRC) using Snowflake SQL on both the training and validation datasets. A significant difference indicates overfitting. Then, retrain the model in Snowpark ML with added L1 or L2 regularization, adjusting the regularization strength based on validation set performance, and redeploy the UDF.

Answer: D,E

Explanation:
Options A and C are correct because they describe strategies for detecting and mitigating overfitting during the model validation process using Snowflake's capabilities. AUPRC is a good performance metric to compare the training vs validation set results to catch overfitting, and regularization can be used to avoid overfitting. Option C directly incorporates cross-validation into the model training workflow within Snowflake, allowing for early detection and mitigation of overfitting through hyperparameter tuning and model selection. Option B is incorrect because it focuses on performance optimization, not overfitting. Option D describes an AIB testing or champion-challenger setup which could be a strategy to use to detect data drift over time, but not overfitting. E is only partially correct as it describes one way to detect data drift, but not overfitting.

NEW QUESTION # 262
You have deployed a fraud detection model in Snowflake and are monitoring its performance. The initial AUC was 0.92. After a month, you observe the AUC has dropped to 0.78. You suspect data drift. Which of the following steps should you take FIRST to investigate and address this performance degradation, focusing on efficient resource utilization within Snowflake?

A. Delete the existing model and deploy a pre-trained, generic fraud detection model obtained from a public repository.
B. Increase the complexity of the existing model architecture by adding more layers to the neural network to improve its adaptability.
C. Deploy a new model version with a higher classification threshold to compensate for the increased false positives.
D. Analyze the distributions of key features in the current production data compared to the training data using Snowflake SQL queries and visualization tools. Specifically compare the distributions of features such as transaction amount and time of day. Then, if drift is confirmed, retrain using updated data.
E. Immediately retrain the model using the entire dataset available, scheduling a Snowpark Python UDF to perform the training.

Answer: D

Explanation:
Analyzing feature distributions to identify data drift is the most logical first step. It allows you to pinpoint which features are contributing to the performance degradation before retraining or making more drastic changes. Retraining immediately (A) is wasteful if the problem isn't data drift. Adjusting the classification threshold (C) is a short-term fix but doesn't address the underlying issue. Increasing model complexity (D) can lead to overfitting. Using a generic model (E) might not be suitable for the specific fraud patterns in your data.

NEW QUESTION # 263
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