Supervised Learning DY0-001 Practice Questions (Page 6)

Questions 26–30

1. 26application · medium

   A grocery store chain analyzes millions of transaction receipts to find products frequently bought together. The goal is to place complementary items in the same aisle and to design promotional bundles. The data is purely transactional (basket items), with no target variable. Which technique should the data science team use?

   • ANaive Bayes to find item probabilities
   • BK-nearest neighbors to cluster items
   • CApriori association rules
   • DLogistic regression to predict item co-occurrence
   Reveal Solution Discussion Select an answer first
2. 27application · medium

   A data scientist is building a linear regression model to predict employee salary from years of experience. After fitting the model, they plot the residuals versus the fitted values and observe a clear funnel shape: the spread of residuals increases as fitted values increase. Which assumption of linear regression is violated, and what is the most appropriate next step?

   • AIndependence is violated; add a time-series component
   • BNormality is violated; apply a Box-Cox transformation to the features
   • CHomoscedasticity is violated; use weighted least squares or transform the target
   • DLinearity is violated; add polynomial terms
   Reveal Solution Discussion Select an answer first
4. 28foundation · easy

   What does the ordinary least squares (OLS) method aim to minimize in linear regression?

   • AThe sum of squared residuals
   • BThe correlation between features
   • CThe absolute value of the intercept
   • DThe number of features
   Reveal Solution Discussion Select an answer first
5. 29foundation · easy

   Which theorem forms the mathematical foundation of the Naive Bayes classifier?

   • APythagorean theorem
   • BBayes' theorem
   • CCentral limit theorem
   • DFermat's little theorem
   Reveal Solution Discussion Select an answer first
6. 30foundation · easy

   For which type of task is Naive Bayes particularly well-suited?

   • AClustering unlabeled data
   • BFinding frequent itemsets in transactions
   • CText classification such as spam detection
   • DPredicting continuous house prices
   Reveal Solution Discussion Select an answer first