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You are working with a large dataset of customer transactions in Snowflake. The dataset contains columns like 'customer id' , 'transaction date', 'product category' , and 'transaction_amount'. Your task is to identify fraudulent transactions by detecting anomalies in spending patterns. You decide to use Snowpark for Python to perform time-series aggregation and feature engineering. Given the following Snowpark DataFrame 'transactions_df , which of the following approaches would be MOST efficient for calculating a 7-day rolling average of for each customer, while also handling potential gaps in transaction dates?
A. Use a Snowpark Pandas UDF to calculate the rolling average for each customer after collecting all transactions for that customer into a Pandas DataFrame. Handle missing dates using Pandas functionality.
B. Use'window.partitionBy('customer_id').orderBy('transaction_date').rangeBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with a date range table joined to the transactions, filling in missing days before calculating the rolling average with 'transaction_amount' set to 0 for the inserted days.
C. Use 'window.partitionBy('customer_id').orderBy('transaction_date').rowsBetween(-6, Window.currentRow)' within a 'select' statement and handle any missing dates using 'fillna()' after calculating the rolling average.
D. Use a stored procedure in SQL to iterate over each customer, calculate the rolling average using a cursor and conditional logic for handling missing dates.
E. Use a simple followed by a UDF to calculate the rolling average. Fill in missing dates manually within the UDF.
Answer: B
Explanation:
Option E is the MOST efficient. Using a date range table joined with the transactions DataFrame to fill in missing dates before calculating the rolling average using 'rangeBetween' is more performant than options that involve UDFs or procedural logic. Options A, C, and D introduce overhead with UDFs or stored procedures which can be slow for large datasets. Option B is less flexible in handling missing dates because 'rowsBetween' considers only the row number, not the actual date difference, potentially leading to inaccurate averages when there are gaps in dates.
NEW QUESTION # 18
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?
A. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
B. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
C. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
D. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.
E. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.
Answer: B,D
Explanation:
Options C and E are the most effective for identifying outliers, considering the skewed nature of transaction data and location-specific variations. The IQR is better than mean and Standard Deviation. The MAD is more robust to outliers compared to standard deviation, which may be inflated by extreme values. Partitioning by location allows for a more nuanced identification of outliers specific to each location. DBSCAN is a great option to include with the partitioning because it considers transaction amount, location, and time as a factor in determine whether the data is an outlier. A and B are less effective because the median and standard deviation are sensitive to extreme values, and the IQR will not consider other dimensions such as location and time. D is only okay because it does not consider the impact of location on determining outliers.
NEW QUESTION # 19
You are developing a model to predict house prices based on structured data including size, number of bedrooms, location, and age. You have built a linear regression model within Snowflake. During the evaluation, you observe that the residuals exhibit heteroscedasticity. Which of the following actions is the LEAST appropriate to address heteroscedasticity in this scenario, considering you want to implement the solution primarily using Snowflake's built-in features and capabilities?
A. Transform independent variables using Box-Cox transformation and include in Snowflake Linear Regression Model Training
B. Implement Weighted Least Squares (WLS) regression by calculating weights inversely proportional to the variance of the residuals for each data point. This involves creating a UDF to calculate weights and modifying the linear regression model fitting process. (Assume direct modification of the fitting process is possible within Snowflake).
C. Apply a logarithmic transformation to the target variable ('SALES_PRICE) using the 'LOG' function within Snowflake before training the linear regression model.
D. Include interaction terms between the independent variables in your linear regression model.
E. Use robust standard errors in the linear regression analysis, even though Snowflake doesn't directly support calculating them. You decide to export model coefficients to an external statistics package (e.g., Python with Statsmodels) to compute robust standard errors and then bring insights back to Snowflake.
Answer: E
Explanation:
Option C is the least appropriate because it requires exporting model coefficients to an external tool to calculate robust standard errors. While robust standard errors are a valid way to address heteroscedasticity's impact on inference (hypothesis testing), the question explicitly prioritizes using Snowflake's built-in capabilities. Options A, B, D and E involve transformations/modifications within Snowflake itself. Applying a logarithmic transformation (A) can stabilize variance. Implementing WLS (B) directly addresses the unequal variances. Including interaction terms (D) can capture non-linear relationships and address some heteroscedasticity. Box-Cox Transformation (E) is a general way to transform non-normal independent variables.
NEW QUESTION # 20
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?
A. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
B. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
C. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
D. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
Answer: A
Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.
NEW QUESTION # 21
You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?
A. Z-score standardization:
B. Box-Cox transformation:
C. Z-score standardization after clipping values outside 1 and 99 percentile:
D. Min-Max scaling to the range [0, 1]:
E. Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):
Answer: E
Explanation:
Option D is the most appropriate. Given the existing constraint on 'customer_age' (0-120), and the requirement to avoid stored procedures, directly scaling to the range [0, 1] using the known minimum and maximum values is efficient and guarantees the output remains within a predictable range. This approach avoids data-dependent calculations (like MIN and MAX over the entire dataset) which are unnecessary given the constraint. Option A won't guarantee values within [0, 1]. Option B is correct but option D is the efficient solution to get the expected outcome and avoid cost and complexity. Option C would not scale to between O and 1 and adds complexity. Option E is not a normalization technique.
NEW QUESTION # 22
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