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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

A) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
B) Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.
C) Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
D) Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
E) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).

2. You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

3. 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) Accuracy - as it measures the overall correctness of the model.
C) F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
D) Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
E) Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.

4. You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A) Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
B) Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.
C) Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.
D) Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
E) All of the above.

5. You are performing exploratory data analysis on a dataset of customer transactions in Snowflake to prepare for a linear regression model that predicts transaction value based on several customer-related features (e.g., age, location, number of previous transactions). You suspect a non-linear relationship between 'customer_age' and 'transaction_value'. Which of the following Snowflake SQL techniques is MOST appropriate for exploring and potentially transforming the 'customer_age' variable to better fit a linear regression model?

A) Implement a Box-Cox transformation in Snowpark Python, select a suitable transformation parameter based on the data, and apply the transformation on 'customer_age' feature.
B) Use the window function to bin 'customer_age' into quartiles and treat each quartile as a categorical variable in the linear regression model.
C) Calculate the Pearson correlation coefficient between 'customer_age' and 'transaction_value' using the function. If the correlation is low, discard the 'customer_age' variable.
D) Apply a logarithmic transformation to 'customer_age' if a scatter plot of 'customer_age' vs 'transaction_value' shows a curve that flattens out as 'customer_age' increases.
E) Create polynomial features by adding 'customer_ageA2' and 'customer_ageA3' as new columns to the table, without checking for interaction effects.

Solutions: