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CS 7643 Quiz 1 | Actual Questions and Answers
Latest Updated 2025/2026 (Graded A+) Georgia
Institute of Technology
1. Which of the following is False about parametric models?
A. Softmax Regression Model is one of them B. The number of parameters is associated with the number of data, not the dimension of data features C. They try to model a function D. Can return probability score per class, with labels acquired via argmax function Correct Answer: B
2. Which of the following is a non-parametric model?
A. Neural Networks B. Naïve Bayes C. Logistic Regression D. K-NN Correct Answer: D
3. Which of the following is a key drawback of non-parametric models such as K- NN?
A. They assume a fixed number of parameters B. They cannot handle nonlinear boundaries C. They require storing large amounts of training data for prediction D. They are less flexible in modeling complex functions Correct Answer: C
4. In Logistic Regression, the decision boundary is defined by:
A. A nonlinear function of the input features B. A linear combination of input features passed through a sigmoid C. A similarity measure based on Euclidean distance D. A hierarchical tree-based partitioning of data Correct Answer: B
5. The main difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) is:
A. MLE incorporates priors, MAP does not B. MAP incorporates priors, MLE does not C. Both incorporate priors but in different ways D. Neither uses priors, they are identical Correct Answer: B
6. Which of the following is not a property of Naïve Bayes?
A. Assumes independence among features B. Works well with high-dimensional data C. Requires storing all training examples for prediction D. Uses Bayes’ theorem for classification Correct Answer: C
7. Which of the following activation functions is most likely to suffer from the vanishing gradient problem?
A. ReLU B. Tanh C. Sigmoid
D. Theioptimizationispeediofigradientidescent CorrectiAnswer:iC
12. Whichialgorithmifindsitheihyperplaneithatimaximizesimarginibetweenitwoicl asses?
A. DecisioniTrees B. SupportiVectoriMachinesi(SVM) C. NaïveiBayes D. K-Means CorrectiAnswer:iB
13. L1iregularizationi(Lasso)iencourages:
A. Largeiweights B. Sparseiweightsi(manyizeros) C. Smoothidecisioniboundaries D. Nonlineariinteractions CorrectiAnswer:iB
14. IniK-meansiclustering,iKirepresents:
A. Numberiofifeatures B. Numberioficlusters C. Numberiofinearestineighbors D. Numberiofiiterations CorrectiAnswer:iB
15. Whichiofitheifollowingiisiaiconvexifunction?
A. Sigmoidiactivation B. MeaniSquarediErroriloss
C. ReLUiactivation D. Cosineisimilarity CorrectiAnswer:iB
16. Whichioptimizeriadaptsilearningiratesiperiparameteriusingifirstiandisecondim omentiestimates?
A. SGD B. Adam C. RMSProp D. AdaGrad CorrectiAnswer:iB
17. Inigradientidescent,itooilargeiailearningirateicanicause:
A. Fastericonvergence B. Stuckiinilocaliminima C. Oscillationsioridivergence D. Underfitting CorrectiAnswer:iC
18. Overfittingioccursiwhen:
A. Modeliperformsiwellionitrainingidataibutipoorlyionitestidata B. Modelihasitooifewiparameters C. Modeliisitooisimpleitoicaptureiunderlyingistructure D. Trainingilossiremainsihigh CorrectiAnswer:iA
19. Whichiofitheifollowingiisi noti aikernelifunctioniusediiniSVMs?
A. Itiincreasesitheinumberiofifeatures B. Itiisisupervised C. Itireducesidimensionalityibyiprojectingiontoidirectionsiofimaximumivariance D. Itimaximizesiclassificationiaccuracy CorrectiAnswer:iC
24. Iniaiconfusionimatrix,ifalseinegativesicorrespondito:
A. Predictedipositive,iactualinegative B. Predictedinegative,iactualipositive C. Predictedipositive,iactualipositive D. Predictedinegative,iactualinegative CorrectiAnswer:iB
25. Whichimachineilearningiparadigmiisiusediinireinforcementilearning?
A. Labeledidata B. Reward-basedilearningiviaiinteractioniwithienvironment C. Clusteringiofiunlabeledidata D. Lineariclassificationiboundaries CorrectiAnswer:iB
26. Whichiofitheifollowingibestiexplainsitheiuniversaliapproximationitheoremif orineuralinetworks?
A. Aisingle-layerilinearinetworkicaniapproximateianyifunction B. Aideepienoughifeedforwardinetworkiwithinonlineariactivationsicania pproximateianyicontinuousifunction C. Neuralinetworksialwaysioutperformitraditionalimodels D. Anyimodelicaniapproximateidataiifigivenienoughitrainingitime CorrectiAnswer:iB
