Factors Involved in ML Algorithm Selection CT-AI Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Which of the following is a key factor that influences the choice of an ML algorithm?

   • AThe brand of the computer hardware
   • BThe name of the dataset file
   • CThe need for model interpretability
   • DThe office location of the team
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   A bank wants to predict whether a loan applicant will default (yes/no). What type of ML problem is this?

   • ADimensionality reduction
   • BClustering
   • CRegression
   • DClassification
   Reveal Solution Discussion Select an answer first
4. 8expert · hard

   A ride-sharing company wants to predict the duration of trips. They have a dataset of 50 million trips with features like distance, time of day, traffic, and weather. The model must be retrained every night on a limited cluster of 100 CPU cores. The company needs predictions in real-time for the app. Which algorithm is most suitable?

   • ADeep neural network
   • BSupport vector machine with RBF kernel
   • CGradient boosting with early stopping
   • DK-nearest neighbors
   Reveal Solution Discussion Select an answer first
5. 9expert · hard

   A hospital wants to predict patient mortality risk to allocate ICU resources. The model must be interpretable for clinicians to trust and act on. The dataset has 200,000 records and 300 features. The team has tested a deep learning model with 92% AUC and a logistic regression model with 85% AUC. The hospital has a policy that any model used in clinical decisions must be explainable. Which model should they choose?

   • ADeep learning model, because it has higher AUC
   • BLogistic regression, because it meets the explainability policy
   • CA random forest with feature importance
   • DDeep learning model with LIME explanations
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   A logistics company wants to predict delivery delays based on route, weather, and traffic data. They have a streaming data pipeline that requires the model to be retrained every hour. The infrastructure is limited to CPU-only servers. The model needs to handle both numerical and categorical features. Which algorithm is most suitable?

   • ADeep neural network
   • BSupport vector machine with RBF kernel
   • CLogistic regression
   • DK-nearest neighbors
   Reveal Solution Discussion Select an answer first