Objective 4.2 Train Models CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What does Bayesian optimization use to model the objective function?

   • AA linear regression model
   • BA decision tree
   • CA neural network
   • DA probabilistic surrogate model, such as a Gaussian process
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   What does the learning rate in gradient descent control?

   • AThe number of features used in the model
   • BThe size of the steps taken toward the minimum of the loss function
   • CThe number of training epochs
   • DThe accuracy of the model
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Why is hyperparameter tuning important in model training?

   • AIt reduces the size of the dataset
   • BIt eliminates the need for feature engineering
   • CIt improves model performance and generalization
   • DIt guarantees the model will be perfectly accurate
   Reveal Solution Discussion Select an answer first
5. 9expert · hard

   A research team is working on a sentiment analysis task for a niche domain with limited labeled data. They have access to a large pre-trained transformer model. Which approach is most effective to adapt the model to their specific task?

   • ATrain a new transformer model from scratch on the limited data
   • BFine-tune the pre-trained model on the labeled domain data
   • CUse the pre-trained model without any adaptation
   • DUse a simple linear model on the raw text
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   What is the output of hierarchical clustering?

   • AA single cluster containing all data points
   • BA tree-like structure called a dendrogram
   • CA probability distribution over clusters
   • DA set of k cluster centroids
   Reveal Solution Discussion Select an answer first