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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?

A) Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
B) Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
C) Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
D) Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
E) Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.

2. You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

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) 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.

4. You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?

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

5. You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

A)

B)

C)

D)

E)

Solutions: