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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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 'loan_amount I applicant_age' .
B) 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)
C) Calculated as 'applicant_income I loan_amount'.
D) Calculated as 'applicant_age / applicant_income'.
E) Calculated as 'applicant_age applicant_age'.

2. You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

A) AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
B) Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
C) Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
D) Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
E) Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.

3. You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

A) Option A
B) Option D
C) Option C
D) Option B
E) Option E

4. You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

A) Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
B) Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.
C) Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
D) Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.
E) Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.

5. You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A) Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
B) Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
C) The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
D) The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
E) Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.

Solutions: