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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is 'is_fraudulent' (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

A) Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.
B) Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.
C) Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.
D) Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.
E) Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.

2. A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?

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

3. You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

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

4. You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A) Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
B) Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.
C) Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.
D) Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.
E) Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.

5. You have a table in Snowflake named 'CUSTOMER DATA' with columns 'CUSTOMER D', 'PURCHASE AMOUNT', and 'RECENCY'. You want to perform feature scaling on 'PURCHASE AMOUNT' using Min-Max scaling and store the scaled values in a new column named 'SCALED PURCHASE _ AMOUNT'. Which of the following Snowflake SQL code snippets correctly implements this feature scaling? Note: Assume there are no NULL values in PURCHASE AMOUNT and you have privileges to create temporary tables and UDFs if necessary.