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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A) Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
B) Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
C) Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
D) Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.
E) Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.

2. You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

A) Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
B) Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
C) Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
D) Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
E) Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.

3. 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) 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.
B) 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.
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) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
E) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.

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) 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.
B) 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.
C) Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.
D) 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.
E) 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.

5. You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?

A) Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
B) Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
C) Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
D) Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
E) Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.

Solutions: