Other Applications DY0-001 Practice Questions (Page 7)

Questions 31–35

1. 31foundation · easy

   Which data science method is commonly used to detect fraudulent credit card transactions?

   • ATopic modeling on transaction descriptions
   • BAnomaly detection on transaction features
   • CImage classification on transaction receipts
   • DSentiment analysis on customer reviews
   Reveal Solution Discussion Select an answer first
2. 32application · medium

   An e-commerce company wants to detect fraudulent transactions in real-time. They have a highly imbalanced dataset where fraudulent transactions are rare (0.1% of all transactions). Which approach is most effective for building a fraud detection model?

   • AUse a random forest model with default settings, which will handle the imbalance automatically.
   • BApply a simple threshold on transaction amount to flag high-value transactions as fraudulent.
   • CUse anomaly detection techniques like Isolation Forest, which do not require balanced data and can identify rare fraudulent patterns.
   • DTrain a standard logistic regression model on the imbalanced dataset without any adjustments.
   Reveal Solution Discussion Select an answer first
4. 33foundation · easy

   In graph theory, what is a 'path'?

   • AA node that has a high number of connections
   • BA single edge that connects two nodes
   • CA subgraph where every node is connected to every other node
   • DA sequence of edges that connects a sequence of distinct nodes
   Reveal Solution Discussion Select an answer first
5. 34application · medium

   A data science team is building a system to automatically adjust the temperature and lighting in a smart building to minimize energy costs while maintaining occupant comfort. The system receives feedback on occupant satisfaction and energy usage after each adjustment. Which approach is most appropriate for this sequential decision-making problem?

   • AApply a clustering algorithm to group similar days and use the average settings from each cluster.
   • BUse a reinforcement learning agent that learns a policy to select temperature and lighting settings based on the reward signal from energy cost and occupant satisfaction.
   • CImplement a rule-based system with fixed thresholds for temperature and lighting based on time of day and occupancy.
   • DTrain a supervised regression model on historical data to predict the optimal temperature and lighting settings for each time of day.
   Reveal Solution Discussion Select an answer first
6. 35foundation · easy

   Which algorithm is commonly used in fraud detection to identify rare fraudulent transactions?

   • AK-means clustering
   • BRandom Forest
   • CPrincipal Component Analysis
   • DApriori algorithm
   Reveal Solution Discussion Select an answer first