IBM A1000-047 - Assessment: Foundations of AI Intermediate Practice Exam: Medium Difficulty 2025

A retail company wants to implement an AI system that can understand customer inquiries in natural language and provide personalized product recommendations based on past purchase history. Which combination of AI capabilities would be most appropriate for this use case?

An insurance company is developing a claims processing system using IBM Watson. They need to extract specific information like policy numbers, dates, and claim amounts from unstructured documents. Which IBM Watson service would be most suitable for this requirement?

A data scientist is building a supervised learning model to predict customer churn. After training the model, they notice it performs exceptionally well on training data (98% accuracy) but poorly on test data (65% accuracy). What problem is the model experiencing and what should be done?

A healthcare organization is implementing an AI system to assist doctors in diagnosing rare diseases. According to AI ethics best practices, which approach should be prioritized to ensure responsible AI deployment?

A manufacturing company wants to use AI to predict equipment failures before they occur. They have historical sensor data including temperature, vibration, and pressure readings, along with records of past failures. Which type of machine learning approach is most appropriate for this predictive maintenance scenario?

A company is building a chatbot using IBM Watson Assistant to handle customer service inquiries. Users are complaining that the chatbot frequently misunderstands their questions and provides irrelevant responses. What is the most effective strategy to improve the chatbot's performance?

A financial services company is developing an AI model for loan approval decisions. During testing, they discover the model shows lower approval rates for certain demographic groups despite similar creditworthiness. What principle of AI governance is being violated and what should be the immediate action?

A data science team is selecting features for a predictive model. They have 50 potential features but computational resources are limited. Which approach best balances model performance with efficiency?

An e-commerce company wants to implement an AI solution that can automatically categorize product images, respond to customer questions about products, and recommend similar items. Which combination of IBM Watson services would best address all three requirements?

A neural network is being trained for image classification, but the training process is very slow and the model struggles to learn patterns effectively. The architecture has 2 hidden layers with 10 neurons each. What adjustment would most likely improve both learning effectiveness and training efficiency?