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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
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) Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
D) Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.
E) Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.

2. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) The production environment has significantly more missing data compared to the training and validation environments.
B) Your training and validation sets are not representative of the real-world production data due to sampling bias.
C) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
D) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
E) The AUC metric is inherently unreliable and should not be used for model evaluation.

3. You are training a binary classification model in Snowflake using Snowpark to predict customer churn. The dataset contains a mix of numerical and categorical features, and you've identified that the 'COUNTRY' feature has high cardinality. You observe that your model performs poorly for less frequent countries. To address this, you decide to up-sample the minority classes within the 'COUNTRY' feature before training. Which combination of techniques would be MOST appropriate and computationally efficient for up-sampling in this scenario within Snowflake, considering you are working with a large dataset and want to minimize data shuffling across the network?

A) Use a stored procedure written in Python to iterate through each unique country, identify minority countries, and then use Snowpark to up-sample those countries using 'DataFrame.sample()' with replacement. This offers the most flexibility but introduces significant overhead due to context switching.
B) Utilize Snowflake UDFs (User-Defined Functions) written in Java to perform stratified sampling on the 'COUNTRY' feature, ensuring each minority class is adequately represented in the up-sampled dataset. UDFs allow for complex logic but can be challenging to debug within Snowflake.
C) Use Snowpark's 'DataFrame.groupBy()" and 'DataFrame.count()' to identify minority countries. Then, for each minority country, use DataFrame.unionByName()' to combine the original data with multiple copies of the minority country's data, created using 'DataFrame.sample()' with replacement. This minimizes data movement within Snowflake.
D) Use the 'SAMPLE clause in Snowflake SQL with 'REPLACE' for each minority country, creating separate temporary tables and then combining them with UNION ALL'. This is efficient for small datasets but scales poorly with high cardinality.
E) Leverage Snowpark's 'DataFrame.collect()' to bring the entire dataset to the client machine, then use Python's scikit-learn library for up-sampling. This is suitable only for small datasets as it incurs significant network overhead.

4. You've deployed a fraud detection model in Snowflake using Snowpark. You are monitoring its performance and notice a significant decrease in recall, while precision remains high. This means the model is missing many fraudulent transactions. The training data was initially balanced, but you suspect that recent changes in user behavior have skewed the distribution of fraudulent vs. non-fraudulent transactions in production. Which of the following actions are MOST appropriate to address this issue and improve the model's performance, considering best practices for model retraining within the Snowflake ecosystem?

A) Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.
B) Adjust the model's classification threshold to be more sensitive, even if it means accepting a slightly lower precision. This can be done directly within Snowflake using a SQL UDF that transforms the model's output probabilities.
C) Implement a data drift monitoring system in Snowflake to automatically detect changes in the input features of the model. Trigger an automated retraining pipeline when significant drift is detected. This retraining should include recent production data with updated labels, but only if label data collection can be automated.
D) Retrain the model using a dataset that includes recent production data, being sure to re-balance the dataset to maintain a roughly equal number of fraudulent and non-fraudulent transactions. Prioritize transactions from the last month.
E) Retrain the model using the original training data. Since the precision is high, the model's fundamental logic is still sound. A larger training dataset isn't necessary.

5. 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) 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.
C) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
D) 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.
E) 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.

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