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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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 B
B) Option C
C) Option A
D) Option D
E) Option E

2. You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

3. You are responsible for deploying a fraud detection model in Snowflake. The model needs to be validated rigorously before being put into production. Which of the following actions represent the MOST comprehensive approach to model validation within the Snowflake environment, focusing on both statistical performance and operational readiness, and using Snowflake features for validation?

A) Conducting a comprehensive backtesting analysis using historical data, simulating real-world scenarios, and evaluating the model's performance under different conditions. Using Snowflake's time travel feature to access historical data snapshots for accurate backtesting. Monitoring model performance using Snowflake alerts triggered by custom SQL queries against model prediction logs.
B) Implementing K-fold cross-validation using Snowflake stored procedures and temporary tables to store and aggregate the results from each fold. Evaluating the model's performance across different data segments and time periods to assess its robustness. Using Snowflake streams and tasks to automate the validation process on new incoming data.
C) Performing a single train/test split of the historical data and evaluating model performance metrics (e.g., accuracy, precision, recall) on the test set using standard Python libraries within a Snowflake Snowpark environment. Deploying the model directly if the metrics exceed a predefined threshold.
D) Relying on a simple visual inspection of model outputs and comparing them to a small sample of known fraud cases. Skipping formal validation to accelerate the deployment process.
E) Calculating only the AUC (Area Under the Curve) metric on the entire dataset without performing any data splitting or cross-validation. Deploying the model if the AUC is above 0.7.

4. You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?

A)

B)

C)

D)

E)

5. You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

A)

B)

C)

D)

E)

Solutions: