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NEW QUESTION 25
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Uniform distribution
• B. Binomial distribution
• C. Normal distribution
• D. Poisson distribution

Answer: B

NEW QUESTION 26
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

• A. Configure sampled Shapley explanations on Vertex Explainable AI.
• B. Configure integrated gradients explanations on Vertex Explainable AI.
• C. Train local surrogate models to explain individual predictions.
• D. Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: C

NEW QUESTION 27
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

• A. Last observation carried forward
• B. Mean substitution
• C. Listwise deletion
• D. Multiple imputation

Answer: D

Explanation:
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html

NEW QUESTION 28
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

• A. One feature obtained as an element-wise product between latitude, longitude, and car type
• B. Three individual features binned latitude, binned longitude, and one-hot encoded car type
• C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
• D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

Answer: B

NEW QUESTION 29
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