Core AI Concepts and Business Applications CAIPM Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A retail chain wants to implement AI for demand forecasting. The data science team has built a model that is 95% accurate on historical data. The operations team wants to use the forecasts to automate inventory replenishment. However, the supply chain manager is concerned about the model's performance during promotional events and holidays, which are not well represented in the training data. What is the best approach?

   • AAbandon the AI initiative because the model is not perfect.
   • BUse the model only for non-promotional periods and ignore it during holidays.
   • CFully automate replenishment and trust the model's historical accuracy.
   • DDeploy the model with a human-in-the-loop review for promotional periods and holidays.
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   Which of the following is a strategic benefit that AI can bring to a business?

   • AEnabling data-driven decision-making by uncovering patterns and insights from large datasets.
   • BEliminating all risks associated with business operations.
   • CProviding a one-time solution that requires no ongoing maintenance or updates.
   • DGuaranteeing a competitive advantage that cannot be replicated by competitors.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   What is a significant challenge when implementing AI in a business environment?

   • AAI systems are unable to handle unstructured data such as text and images.
   • BAI implementation always requires replacing existing IT infrastructure completely.
   • CAI models are inherently unexplainable and cannot be audited.
   • DThe need for large volumes of high-quality, relevant data to train effective models.
   Reveal Solution Discussion Select an answer first
5. 9expert · hard

   A manufacturing company wants to implement AI for quality control. They have two options: a computer vision system that inspects products on the assembly line, and a machine learning model that predicts defects based on sensor data from the machines. The company has limited budget and wants to maximize the return on investment. The production line is already equipped with sensors, but there is no existing camera infrastructure. What is the most cost-effective approach?

   • AStart with the machine learning model using existing sensor data, and add computer vision later if needed.
   • BInvest in the computer vision system because it provides more accurate defect detection.
   • CUse neither system because AI is too expensive for quality control.
   • DImplement both systems simultaneously to maximize accuracy.
   Reveal Solution Discussion Select an answer first
6. 10expert · hard

   A telecom company wants to use AI to predict customer churn and offer retention incentives. The marketing team wants to target high-value customers, but the model's predictions are not perfectly accurate. The company is concerned about the cost of offering incentives to customers who would not have churned. What is the best way to manage this trade-off?

   • AOffer incentives to all customers predicted to churn, regardless of accuracy.
   • BUse the model's probability scores to target only customers with a very high predicted churn probability.
   • CIgnore the model and rely on the marketing team's intuition.
   • DOffer incentives to all customers to ensure no churn is missed.
   Reveal Solution Discussion Select an answer first