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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A) Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
B) Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
C) Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
D) Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
E) Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.

2. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) The production environment has significantly more missing data compared to the training and validation environments.
B) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
C) The AUC metric is inherently unreliable and should not be used for model evaluation.
D) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
E) Your training and validation sets are not representative of the real-world production data due to sampling bias.

3. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

4. You are developing a Python stored procedure in Snowflake to train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?

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

5. You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

Solutions: