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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A) Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction).
B) Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).
C) Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables.
D) Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made.
E) Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions.

2. You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

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

3. Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A) Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
B) Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
C) Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
D) Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.
E) Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.

4. You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?

A) Robust scaling using interquartile range (IQR) in a stored procedure with Python:

B) Z-score standardization using the following SQL:

C) Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:

D) Unit Vector normalization (L2 Normalization) using SQL:

E) Min-Max scaling using the following SQL:

5. You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A) Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.
B) Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
C) Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.
D) The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
E) Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.

Solutions: