Probability and Modeling DY0-001 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   A distribution has high kurtosis. What is the most likely implication for the data?

   • AThe mean is significantly different from the median.
   • BThe data is uniformly distributed.
   • CThe distribution is perfectly symmetric.
   • DThere are more extreme outliers than in a normal distribution.
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2. 17foundation · easy

   What is heteroskedasticity in a data set?

   • AThe errors are independent of each other.
   • BThe mean of errors is not zero.
   • CThe variance of errors is not constant across observations.
   • DThe errors are normally distributed.
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4. 18application · medium

   A data scientist is building a linear regression model to predict housing prices based on square footage. After fitting the model, the scientist plots the residuals against the fitted values and observes that the spread of residuals increases as the fitted values increase. This pattern is most indicative of which condition?

   • AHeteroskedasticity
   • BSkewness in the residuals
   • CAutocorrelation
   • DMulticollinearity
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5. 19application · medium

   A data scientist is working on a fraud detection model where fraudulent transactions represent only 1% of the data. The initial model has high accuracy but very low recall for the fraud class. The scientist wants to improve the model's ability to catch fraud without changing the algorithm. Which approach is most directly aimed at this problem?

   • ARemoving all non-fraudulent transactions
   • BOversampling the minority class using SMOTE
   • CIncreasing the number of features in the model
   • DStratifying the test set by transaction amount
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6. 20application · medium

   A data analyst is modeling the number of website visits per hour. The analyst has determined that the number of visits follows a Poisson distribution with a mean of 5 visits per hour. What is the probability that the website receives exactly 3 visits in the next hour?

   • ADivide the mean by 3 to get the probability
   • BUse the cumulative distribution function (CDF) with λ=5 and k=3
   • CUse the probability density function (PDF) with λ=5 and x=3
   • DUse the probability mass function (PMF) with λ=5 and k=3
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