You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?
Correct Answer: B,D
Options B and D provide a balanced approach for both technical and non-technical audiences- A confusion matrix (Option B) is easily understandable and shows model performance across different prediction outcomes. A summary plot of SHAP values clearly illustrates feature importance and direction of impact. A line chart showing cumulative churn rate across different customer segments highlights the business value-Option D is also highly effective because scatter plots can be easily understood, especially when colored by churn prediction- The table of model metrics provides necessary details. The waterfall plot brings the explanation down to an individual customer level, making the model's behavior more tangible. Options A, C and E have deficits- Option A lacks detailed performance visualization. Option C is technical and might confuse non-technical stakeholders. Option E has too many summary plots.
Question 87
You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet: You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?
Correct Answer: C,E
Options C and E are the most effective strategies. Option C (Feature Engineering): By creating interaction terms between EQUIPMENT _ ICY and other sensor features, the model can learn equipment-specific patterns. This enables the model to account for the unique characteristics of each equipment group, improving its ability to generalize across all equipment. For example, the optimal temperature threshold for triggering a failure might differ significantly between EQUIPMENT_ID' groups, and this can be captured using interaction terms. Option E (Seperate models per Equipment ID) : Hyperparameter tuning and training separate models per equipment ID enables you to optimize and customize the model specific to each equipment ID. The downsize is that we need to create and manage more models. Options A and D are less effective or may have limitations: Option A (Increase Training Data Size): While increasing the training data size can sometimes improve model performance, it doesn't guarantee that the model will learn to differentiate between the equipment groups effectively, especially if some groups have significantly different data characteristics. This can also consume a lot of resources unnecessarily. Option D (Custom cross Validation) : While it's valid, it is difficult to implement and the built in Snowflake cross validation features is much more performant and easier to use.
Question 88
A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?
Correct Answer: A,C,D,E
The Central Limit Theorem (CLT) allows us to perform a z-test to determine whether the mean of a sample is significantly different from the population mean. The essential steps are: A: Calculate the standard deviation of the population (?) and estimate the standard error. This is necessary to calculate the z-statistic. C: Ensure that samples are drawn independently and randomly. This is a key assumption for the CLT to hold. D: This step uses the samples to estimate the standard error of the mean directly from the 500 calculated sample means. Both A and D are correct, and the analyst could choose either approach depending on the computational efficiency and availability of population data. If population standard deviation is known or easily calculated, that's preferred. However, an estimate from the standard deviation of the sampling distribution is also valid, especially when population standard deviation calculation is not feasible. E: The CLT is applicable only if the sample size is large enough. For many distributions, n=50 is sufficient. We assume replacement, such that population size N >> n.
Question 89
You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?
Correct Answer: B,D
Options B and D are correct because they employ techniques to mitigate overfitting. Option B uses ' RandomizedSearchCV' with cross-validation and a fixed 'random_state' , making the search reproducible and preventing overfitting by evaluating performance on multiple validation sets. Option D leverages 'BayesianSearchCV' , which uses a probabilistic model to efficiently explore the hyperparameter space, also with cross-validation and a fixed random state making search reproducible. Both methods aim to find a balance between model complexity and generalization ability. Option A is incorrect because it does not use cross-validation, which is crucial for preventing overfitting. Option C is incorrect because manual tuning without a systematic search and cross-validation is prone to bias and overfitting. Finally, option E is incorrect because while using a modern algorithm, it lacks a random state, making it difficult to reproduce the outcome.
Question 90
You are training a regression model to predict house prices using a Snowflake dataset. The dataset contains various features, including 'number of_bedrooms', , and You want to use time-based partitioning for your training, validation, and holdout sets. However, you also need to ensure that the dataset is properly shuffled within each time partition to mitigate potential bias introduced by the order of data entry. Which of the following strategies is MOST EFFECTIVE and EFFICIENT for partitioning your data into train, validation, and holdout sets in Snowflake, while also ensuring random shuffling within each partition, and addressing potential data leakage issues?
Correct Answer: E
Option E is the most effective and efficient because it correctly implements the required partitioning and shuffling while minimizing data leakage and maximizing performance. Here's a breakdown: Time-Based Partitioning: The CASE statement accurately divides the data into train, validation, and holdout sets based on 'sale_date' . Random Shuffling Within Partitions: 'ROW NUMBER() OVER (PARTITION BY split_group ORDER BY RANDOM())' calculates a random row number within each split group (train, validation, holdout). This ensures that the data is shuffled within each time-based partition, mitigating bias introduced by the order of data entry, without introducing data leakage. Prevents Data Leakage: Shuffling the data within each partition prevents data leakage that could occur if you shuffle the entire dataset before partitioning. Efficiency: Avoids expensive operations like UDFs or sorting the entire dataset.lJses window functions efficiently to calculate random row numbers within partitions. Option A is not suitable since It does not address shuffling within parition and the shuffle will be affected by other filtering operations later.Option B is not suitable because RANDOM does not work inside create table and if it did it will cause data leakage, because all splits influence the randomness. Option C is not ideal because SAMPLE does not guarantee non-overlapping data, which would undermine the integrity of train/validation/holdout sets, moreover 'order by random()' will only apply the sampling on a sorted result not generate a random sampling. Option D is not suitable because it uses UDFs. UDFs in Snowflake generally have performance overhead compared to native SQL functions. Also using a global 'ORDER BY can be very slow on large datasets and will also introduce data leakage.
