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A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)
A. Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.
B. Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
C. Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
D. Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.
E. Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
Answer: A,C,D,E
Explanation:
Options A, B, C and D all represent valid and useful feature engineering strategies for predicting customer churn. RFM (A) is a classic approach. Calculating the days since last login (B) provides a measure of engagement. Estimating the number of unique product categories purchased (C) offers insights into customer diversity. Tracking activity trends (D) helps identify customers who are becoming less engaged. Option E would be a poor choice, as the 'ACTIVITY TYPE column, if not properly encoded, may not be effective in the machine learning model. One-hot encoding or other transformations are required for categorical features.
NEW QUESTION # 259
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?
A. Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
B. Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
C. Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
D. Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
E. Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
Answer: E
Explanation:
Option B is the MOST effective way to minimize memory consumption within the Python stored procedure. The 'hist' tree method in XGBoost uses a histogram-based approach for finding the best split points, which is more memory-efficient than the exact tree method. Gradient- based sampling ('goss') reduces the number of data points used for calculating the gradients, further reducing memory usage. Tuning 'max_depth' and helps to control the complexity of the trees, preventing them from growing too large and consuming excessive memory. Converting categorical features to numerical is crucial as categorical features when One Hot Encoded, can explode feature space and significantly increase memory footprint. Option A will not work directly within Snowflake as Dask is not supported on warehouse compute. Option C may reduce the accuracy of the model. Option D requires additional infrastructure and complexity. Option E doesn't directly address the memory issue during the training phase, although early stopping is a good practice, the underlying memory pressure will remain.
NEW QUESTION # 260
You're building a linear regression model in Snowflake to predict house prices. You have the following features: 'square_footage', 'number of bedrooms', 'location id', and 'year built'. 'location id' is a categorical variable representing different neighborhoods. You suspect that the relationship between 'square footage' and 'price' might differ based on the 'location id'. Which of the following approaches in Snowflake are BEST suited to explore and model this potential interaction effect?
A. Create interaction terms by multiplying 'square_footage' with one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
B. Apply a power transformation to 'square_footage' before including it in the linear regression model. This correct, but only to one variable.
C. Create interaction terms by adding 'square_footage' and one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
D. Use the 'QUALIFY clause in Snowflake SQL to filter the data based on 'location_id' before calculating regression coefficients. This is incorrect approach.
E. Fit separate linear regression models for each unique 'location_id', using 'square_footage', 'number_of_bedrooms', and 'year_built' as independent variables.
Answer: A
Explanation:
Creating interaction terms by multiplying 'square_footage' with one-hot encoded columns from 'location_id' allows the model to estimate different slopes for 'square_footage' for each location. This directly models the interaction effect. Fitting separate models might be computationally expensive and does not allow for sharing of information across locations. The QUALIFY clause is used for filtering and not directly relevant to modeling interactions. A power transformation only affects 'square_footage' and not the interaction effect. Adding instead of multiplying will not create an interaction.
NEW QUESTION # 261
A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?
A. Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
B. Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.
C. Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
D. Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.
E. Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
Answer: E
Explanation:
Option C is the most effective. Using a variance threshold directly addresses multicollinearity by removing redundant features. Creating a geographical region feature from 'state' reduces dimensionality and is more manageable than one-hot encoding for high cardinality features. A custom UDF can be used for efficient regional mapping. While PCA can reduce dimensionality, it can also make the features less interpretable. Target encoding (B) can introduce target leakage if not handled carefully. VIF calculation (D) is useful but doesn't directly address the high cardinality of 'state'. Label encoding (E) is not appropriate for nominal categorical features like 'state' as it introduces ordinality.
NEW QUESTION # 262
A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?
A. Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
B. The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
C. The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
D. The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
E. The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
Answer: B,C,E
Explanation:
Option A is correct because support represents the proportion of transactions that contain both the antecedent and the consequent. Option D is correct because confidence represents the proportion of transactions containing the antecedent that also contain the consequent. Option E is correct because lift = confidence / (probability of consequent). Therefore, lift of 1.2 means confidence is 1.2 times the probability of the consequent. Hence 20% more likely than the baseline. Option B is incorrect because lift, not confidence, captures the relative likelihood compared to the baseline. Option C is incorrect because a lift > 1 suggests a positive correlation, not a negative one.
NEW QUESTION # 263
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