Objective 3.2 Optimize the Algorithm (e.g., Structure, Run Time, Tuning Hyperparameters) AIP-210 Practice Questions (Page 5)

Questions 21–25

1. 21application · medium

   A team is training a decision tree on a large dataset. The tree is very deep and training is slow. They want to reduce training time while maintaining accuracy. Which structural modification is most appropriate?

   • AUse a larger training set to improve accuracy
   • BIncrease the number of features considered at each split
   • CLimit the maximum depth of the tree
   • DUse a deeper tree to capture more patterns
   Reveal Solution Discussion Select an answer first
2. 22expert · hard

   A team has two candidate algorithms for a real-time prediction system. Algorithm A has O(n) time complexity and Algorithm B has O(log n) time complexity, where n is the number of features. The team has a large number of features (n = 1,000,000). Which algorithm is more suitable for meeting the real-time requirement?

   • AAlgorithm A, because O(n) is linear and predictable
   • BAlgorithm A, because it is simpler to implement
   • CAlgorithm B, because O(log n) scales much better than O(n) for large n
   • DBoth are equally suitable because they have the same accuracy
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   What is the primary difference between model parameters and hyperparameters?

   • AModel parameters are always integers, while hyperparameters are always floats
   • BHyperparameters are learned from data, while model parameters are set manually
   • CModel parameters are only used in deep learning, while hyperparameters are used in all models
   • DModel parameters are learned from data, while hyperparameters are set before training
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   What does Big O notation primarily describe in runtime complexity analysis?

   • AThe amount of memory used by the algorithm in bytes
   • BHow the runtime grows as the input size increases
   • CThe exact number of operations for a given input
   • DThe time taken on a specific hardware configuration
   Reveal Solution Discussion Select an answer first
6. 25application · easy

   A team is tuning a neural network. They are deciding which aspects of the model to tune. Which of the following is a hyperparameter, as opposed to a model parameter?

   • AOutput of the softmax layer
   • BLearning rate
   • CBiases of the neural network
   • DWeights of the neural network
   Reveal Solution Discussion Select an answer first