Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03

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Snowflake DSA-C03 試験シラバストピック:

Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題：

1. 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) 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.
C) 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).
D) Using a simple moving average to smooth the data before applying a linear regression model.
E) Applying exponential smoothing directly to the original time series without feature engineering.

2. You're working with a large dataset of user transactions in Snowflake. You need to identify potential outliers in transaction amounts C TRANSACTION AMOUNT) for each user CUSER ID'). Your goal is to flag transactions that are more than 3 standard deviations away from the mean transaction amount for that specific user. Which of the following approaches, utilizing Snowflake's statistical functions and window functions, would be MOST efficient and accurate for achieving this?

A) Using a correlated subquery to calculate the mean and standard deviation for each user and then filtering the transactions.
B) Calculating the overall mean and standard deviation for all transactions and filtering transactions based on those global statistics.
C) Using window functions to calculate the mean and standard deviation for each user within the same query, and then comparing each transaction amount to the calculated range.
D) Exporting the data to a Python environment, performing the calculations using Pandas, and then re-importing the results to Snowflake.
E) Creating a stored procedure that iterates through each user and calculates the mean and standard deviation individually.

3. A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A) Calculated as 'applicant_age applicant_age'.
B) Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
C) Calculated as 'applicant_income I loan_amount'.
D) Calculated as 'loan_amount I applicant_age' .
E) Calculated as 'applicant_age / applicant_income'.

4. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) Your training and validation sets are not representative of the real-world production data due to sampling bias.
B) The production environment has significantly more missing data compared to the training and validation environments.
C) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
D) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
E) The AUC metric is inherently unreliable and should not be used for model evaluation.

5. You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

A) Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).
B) Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.
C) Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
D) Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.
E) Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.

質問と回答：