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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A) Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
B) Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
C) Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
D) Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
E) Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.

2. You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A) Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction).
B) Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).
C) Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made.
D) Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables.
E) Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions.

3. 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) 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.
B) 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.
C) 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.
D) 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.
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.

4. You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A) Precision, optimized with a threshold adjustment to minimize false positives.
B) F 1-Score, optimized to balance precision and recall equally.
C) Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
D) Recall, optimized with a threshold adjustment to minimize false negatives.
E) AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.

5. You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A) Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
B) Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).
C) Using a simple moving average to smooth the data before applying a linear regression model.
D) Applying exponential smoothing directly to the original time series without feature engineering.
E) Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.

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