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

2. A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

A) Reduce the number of features by randomly removing some columns, as this always prevents overfitting.
B) Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
C) Increase the learning rate significantly to speed up convergence during training.
D) Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
E) Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.

3. You are tasked with estimating the 95% confidence interval for the median annual income of Snowflake customers. Due to the non-normal distribution of incomes and a relatively small sample size (n=50), you decide to use bootstrapping. You have a Snowflake table named 'customer_income' with a column 'annual_income'. Which of the following SQL code snippets, when correctly implemented within a Python script interacting with Snowflake, would most accurately achieve this using bootstrapping with 1000 resamples and properly calculate the confidence interval?

A)

B)

C)

D)

E)

4. You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

A) Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
B) Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.
C) Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
D) F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
E) Accuracy - as it measures the overall correctness of the model.

5. You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

A) Reduce the value of 'MAX for the UDF to process smaller batches of data.
B) Partition the input data into smaller chunks using SQL queries and call the UDF on each partition separately.
C) Increase the warehouse size to provide more memory per node.
D) Implement lazy loading of the model within the UDF, ensuring it's only loaded once per warehouse node and reused across multiple invocations within that node.
E) Increase the value of 'MAX BATCH_ROWS' for the UDF to process larger batches of data at once.

Solutions: