Describe How AI Is Used in Varied Industries CERTIFIED-AI-EXPERT Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A logistics company wants to use AI to optimize delivery routes for its fleet of trucks. The company has real-time traffic data, delivery time windows, and vehicle capacity constraints. The operations team needs a solution that can adapt to changing conditions (e.g., accidents, new orders) throughout the day. Which AI technique is best suited for this dynamic routing problem?

   • AA static optimization algorithm that computes the optimal route once at the start of the day for all deliveries.
   • BA reinforcement learning agent that continuously learns and adjusts routes based on real-time feedback and changing conditions.
   • CA rule-based system that uses fixed heuristics to select routes based on distance and delivery windows.
   • DA supervised learning model trained on historical routes to predict the best route for each new delivery request.
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A large e-commerce company wants to increase customer engagement by personalizing product recommendations. They have extensive data on customer browsing history, past purchases, and demographics. The marketing team wants to send personalized email campaigns and display personalized recommendations on the website. Which AI approach best supports this personalization goal?

   • AA hybrid recommendation system that combines collaborative and content-based filtering, and uses customer demographics to segment audiences for email campaigns.
   • BA collaborative filtering recommendation system that suggests products based on the preferences of similar customers.
   • CA simple rule-based system that recommends best-selling products to all customers, with no personalization.
   • DA content-based recommendation system that suggests products similar to those a customer has viewed or purchased in the past.
   Reveal Solution Discussion Select an answer first
4. 13application · medium

   A manufacturing company wants to reduce unplanned downtime on its production line. They have sensors on equipment that stream vibration, temperature, and pressure data. The maintenance team wants to move from reactive repairs to predictive maintenance. The company's leadership needs to see a clear return on investment (ROI) before funding the project. Which AI use case directly addresses the business goal and provides a measurable outcome?

   • ACreate a digital twin of the production line to simulate different operating conditions and optimize throughput.
   • BUse computer vision to inspect finished products for defects and automatically reject defective items from the line.
   • CDeploy a predictive maintenance model that analyzes sensor data to forecast equipment failures and schedules maintenance before breakdowns occur.
   • DImplement a robotic process automation (RPA) bot to automate the work order creation process for maintenance requests.
   Reveal Solution Discussion Select an answer first
5. 14expert · hard

   An energy company wants to use AI to predict equipment failures in wind turbines. They have sensor data from turbines, including vibration, temperature, and power output. The company operates turbines in remote locations with limited connectivity, and the data must be processed on-site to reduce latency and bandwidth usage. Which AI deployment approach best meets the operational constraints?

   • ADeploy a cloud-based AI model that receives sensor data via satellite and sends predictions back to the site.
   • BUse a hybrid approach where the model runs in the cloud but is accessed via a mobile app by on-site technicians.
   • CDeploy an edge AI model on on-site gateways that processes sensor data locally and sends only alerts and summaries to the cloud.
   • DCollect all sensor data on physical drives and ship them to a central location for batch processing.
   Reveal Solution Discussion Select an answer first
6. 15application · medium

   A restaurant chain wants to use AI to forecast daily demand for each menu item at each location. They have historical sales data, local event calendars, weather forecasts, and promotional schedules. The chain wants to reduce food waste and ensure popular items are always in stock. Which AI approach best supports this demand forecasting goal?

   • AA time-series forecasting model that uses historical sales data to predict future demand for each item.
   • BA natural language processing model that analyzes customer reviews to identify popular and unpopular menu items.
   • CA computer vision system that monitors inventory levels in real time and automatically reorders items when stock is low.
   • DA regression model that uses historical sales, weather, local events, and promotions as features to predict demand for each item at each location.
   Reveal Solution Discussion Select an answer first