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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

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

2. 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) 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.
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) 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.
D) 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.
E) 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.

3. You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?

A) Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
B) Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
C) Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
D) Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
E) Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.

4. Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A) Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
B) Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.
C) Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
D) Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
E) Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.

5. A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

A) The CLT is not applicable because the population distribution (call durations) is heavily skewed.
B) The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
C) The CLT is applicable, and the sample mean will converge to the population median.
D) The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
E) The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.

Solutions: