Determine the Differences Between Predictive AI and Generative AI CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which statement about the training of generative AI models is generally true?

   • AGenerative models are often more complex and require more computational resources than predictive models
   • BGenerative models have the same training objectives as predictive models
   • CGenerative models are typically simpler and require less computational power than predictive models
   • DGenerative models require less data than predictive models
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   A game studio wants to automatically create realistic textures for new virtual environments. They have a large collection of existing textures but no labels. Which generative technique is best suited for this task?

   • ATrain a GAN to generate new texture images that mimic the style of the existing collection.
   • BTrain a regression model to predict the color values of new textures.
   • CTrain a time-series model to forecast texture trends.
   • DTrain a classification model to sort textures into categories and reuse them.
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   Which ethical consideration is more specific to generative AI than to predictive AI?

   • AContent authenticity and the potential for deepfakes
   • BModel interpretability for regulatory compliance
   • CBias in predictions leading to unfair decisions
   • DData privacy in training datasets
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   Which model architecture is commonly used for generative AI tasks?

   • AVariational Autoencoders (VAEs)
   • BLogistic regression
   • CSupport vector machines (SVM)
   • DRandom forests
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A team has developed a model to predict customer churn and another to generate new product descriptions. Which evaluation metric is appropriate for the churn prediction model?

   • ADiversity, because it measures the variety of churn predictions.
   • BAccuracy, because it measures the proportion of correct predictions.
   • CRealism, because it measures how realistic the churn predictions are.
   • DCreativity, because it measures the novelty of the churn predictions.
   Reveal Solution Discussion Select an answer first