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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

A) Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.
B) Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
C) Directly use all available columns from each table without any preprocessing to avoid introducing bias.
D) Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.
E) Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.

2. You are tasked with automating the retraining of a fraud detection model in Snowflake. The model is deployed as a Snowflake User-Defined Function (UDF). The training data resides in a Snowflake table named 'TRANSACTIONS. You want to trigger retraining if the model's performance, as measured by AUC, drops below 0.80. The model's AUC is tracked in a Snowflake table named 'MODEL PERFORMANCE. Which of the following strategies provides the MOST efficient and robust solution for automating this retraining process within Snowflake, minimizing latency and external dependencies?

A) Schedule a job on an external system (e.g., a cron job on a Linux server) to periodically query 'MODEL PERFORMANCE and trigger a model retraining process if the AUC is below 0.80. This process would retrain the model externally and update the UDF in Snowflake.
B) Create an external function that is invoked periodically by a Snowflake Task. The external function queries 'MODEL PERFORMANCE and uses a cloud provider's machine learning service (e.g., AWS SageMaker) to retrain the model and update the UDF using Snowflake's external functions capabilities for model deployment.
C) Manually monitor on a dashboard and trigger retraining via a Snowflake Worksheet when needed.
D) Implement a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL_PERFORMANCE. If the AUC is below 0.80, it executes a Snowflake ML pipeline using 'snowflake.ml.modeling' to retrain the model directly within Snowflake and updates the UDF in place using 'CREATE OR REPLACE FUNCTION'.
E) Use a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL PERFORMANCE, and if the AUC is below 0.80, it triggers a Data Engineering pipeline (e.g., using Airflow or Databricks) to retrain the model and update the UDF.

3. You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A) Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.
B) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
C) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
D) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
E) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.

4. You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A) Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
B) Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
C) Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
D) The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
E) The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.

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

Solutions: