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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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)

2. A financial institution wants to use Snowflake Cortex to analyze customer reviews and feedback extracted from various online sources to gauge customer sentiment towards their new mobile banking application. The goal is to identify positive, negative, and neutral sentiments, and also extract key phrases that drive these sentiments. Which of the following steps represent a viable workflow for achieving this using Snowflake Cortex and related functionalities?

A) 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKML.PREDICT' function with the appropriate task-specific model to determine the sentiment score for each review. 3. Further fine-tune the sentiment model with customer review data to improve the score and accuracy.
B) 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKE.ML.PREDICT' function with a sentiment analysis model to determine the overall sentiment score for each review. 3. Apply a separate key phrase extraction model via 'SNOWFLAKE.ML.PREDICT' to identify important keywords in the reviews.
C) 1. Create a Streamlit application hosted externally that connects to the Snowflake database. 2. The Streamlit app uses a Python library like 'transformers' to perform sentiment analysis and key phrase extraction on the customer reviews read from Snowflake. 3. The results are then written back to a separate Snowflake table.
D) 1. Ingest the customer reviews into a Snowflake table. 2. Create a custom JavaScript UDF that calls the Snowflake Cortex 'COMPLETE' endpoint with a prompt that asks for both sentiment and key phrases. 3. Store the results in a new Snowflake table.
E) 1. Ingest the customer reviews into a Snowflake table. 2. Use Snowflake's built-in 'NLP_SENTIMENT' function (if available) or a similar UDF based on a pre- trained sentiment analysis model to get the sentiment score. 3. Use regular expressions in SQL to extract key phrases based on frequency and context.

3. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
B) Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
C) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
D) Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
E) Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.

4. You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A) Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.
B) Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.
C) Select a threshold of 0.5, as this is a common default threshold for binary classification problems.
D) Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.
E) Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.

5. You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?

A) Create a Snowflake task that runs every hour, queries the 'MODEL_PERFORMANCE table, and triggers a Snowpark ML model training script if the accuracy threshold is breached. The training script will overwrite the existing model.
B) Use a Snowflake stream on the 'MODEL_PERFORMANCE table to detect changes in accuracy, and trigger a Snowpark ML model training function using a PIPE whenever the accuracy drops below the threshold.
C) Implement an external service (e.g., AWS Lambda or Azure Function) that periodically queries the "MODEL_PERFORMANCE table using the Snowflake Connector and triggers a Snowpark ML model training script via the Snowflake API.
D) Create a Dynamic Table that depends on the 'MODEL PERFORMANCE table and materializes when the accuracy is below the threshold. This Dynamic Table refresh triggers a Snowpark ML model training stored procedure. This stored procedure saves the new model with a timestamp and updates a metadata table with the model's details.
E) Implement a Snowpark ML model training script that automatically retrains the model every day, regardless of the performance metrics. This script will overwrite the previous model.

Solutions: