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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
B) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
C) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
D) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
E) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.

2. You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A)

B)

C)

D)

E)

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) 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.
B) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
C) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
D) 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.
E) 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.

4. 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) 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.
B) 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.
C) 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.
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.

5. 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) 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.
B) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
C) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
D) 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.
E) 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.

Solutions: