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You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?
A. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
B. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.
C. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
D. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.
E. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
Answer: B
Explanation:
The most appropriate approach is to extract the data and perform the confidence interval calculations outside of the stored procedure using a dedicated statistical environment. Options A and D are less scalable and efficient within the stored procedure. Option B provides insufficient information. Option E is not feasible for dynamic calculation based on changing data.
NEW QUESTION # 156
You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?
A. Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
B. Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
C. Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
D. Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.
E. Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
Answer: B
Explanation:
Option E is the most effective. Configuring the feature group with is important to reduce model staleness during online inference. Setting the in the configuration will serve as an indicator for staleness and use the method to retrieve the latest feature value available.
NEW QUESTION # 157
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. 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.
B. 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.
C. 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).
D. 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.
E. 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.
Answer: A,C,D,E
Explanation:
Options A, C, D, and E are essential. A (Z-score calculation with rolling window) provides a dynamic measure of how unusual a reading is relative to recent history for each sensor. C (DBSCAN clustering) helps identify outliers based on density; points far from any cluster are likely outliers. D (Stored procedure with outlier flagging) automates the outlier detection process and makes it easy to filter and visualize outliers in a dashboard, with a business ready explanation. Option E allows you to focus on the right data, allowing you to have a more useful visualisation. Option B (direct 3D scatter plot without pre-processing) is not effective because it will be difficult to identify outliers visually in a high- density scatter plot without any outlier detection or data reduction. The direct scatter plot becomes overwhelming very quickly with sensor data.
NEW QUESTION # 158
You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?
A. Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
B. Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
C. Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
D. Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
E. Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
Answer: A,B,C,D
Explanation:
Options A, B, C and E are correct because: A: Correctly identifies the benefits (segment-specific models) and drawbacks (overfitting on small segments) of partitioning by 'customer_segment. B: Accurately describes the advantages (temporal patterns, walk-forward validation) and limitations (independence from signup date) of partitioning by 'signup_date' . C: Properly explains the use case (geographic influence), performance benefits (filtering), and potential drawbacks (data silos) of partitioning by 'region'. E: Correctly highlights the implementation overhead and potential skew issues associated with partitioning. Option D is incorrect because Clustering on top of paritioning is not always guranteed performance improvements without assessing underlying query patterns. Snowflake automatically partitions data into micro-partitions, so additional clustering might not always result in significant performance improvements.
NEW QUESTION # 159
You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:
The 'feature_importance_results' table contains the following data:
Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?
A. The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.
B. Permutation importance only reveals the importance of features within the current model. Different models trained with different features or algorithms might have different feature rankings.
C. Increasing the 'contract_length' for customers will always lead to a higher probability of renewal. However, there could be correlation between contract length and monthly charges.
D. The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.
E. The 'contract_length' and 'monthly_charges' features are equally important.
Answer: A,B,D
Explanation:
Option A is correct because permutation importance measures the decrease in model performance after a feature is randomly shuffled. A larger decrease indicates a higher importance. Option B is correct; a small 'mean_auc_decrease' for 'support_calls' indicates it has minimal impact, implying its removal won't drastically affect AUC. Option E is correct as permutation importance is model-specific and dataset-specific. The features considered important may vary across model types or even the samples used in training. Option C is incorrect. 0.25 is not equivalent to 0.15. Option D is incorrect because permutation importance doesn't directly translate to a causal relationship between feature values and the target variable (renewal). There could be confounding factors or non-linear relationships.
NEW QUESTION # 160
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