IBM A1000-083 - Assessment: Foundations of Watson AI v2 Intermediate Practice Exam: Medium Difficulty 2025

A retail company wants to deploy a Watson AI solution that can analyze customer reviews to identify product issues and sentiment trends. The solution needs to extract specific product features mentioned in reviews and categorize them by sentiment. Which combination of Watson AI services would be most appropriate for this scenario?

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

An insurance company is implementing Watson Natural Language Understanding to extract information from claim documents. They need to identify custom entities specific to their industry, such as policy types, claim categories, and specific medical procedures. What approach should they take?

A machine learning team is selecting an algorithm for a classification problem where they need to predict whether a transaction is fraudulent. The dataset is highly imbalanced with only 2% fraudulent transactions. Which evaluation metric would be most appropriate for assessing model performance?

A developer is building a Watson Assistant chatbot for a banking application. They need to handle a scenario where users might ask about account balances in multiple ways (e.g., 'What's my balance?', 'How much money do I have?', 'Show my account balance'). What Watson Assistant feature should they utilize to handle these variations effectively?

A company is developing an AI application using Watson services and needs to implement a data pipeline that preprocesses raw customer feedback data before feeding it to a machine learning model. The pipeline must handle missing values, normalize text data, and remove duplicates. Where should this preprocessing logic ideally be implemented in the Watson AI architecture?

A healthcare organization wants to use Watson Discovery to search through medical research papers and clinical trial documents. They need the system to understand medical terminology and provide relevant answers to physician queries. Which Watson Discovery feature would most improve the relevance of search results for domain-specific medical content?

During the development of a sentiment analysis model, a team discovers that their model performs well on formal business communications but poorly on social media posts with slang, emojis, and abbreviations. What is the most effective strategy to improve model performance across both types of content?

A Watson Assistant application needs to collect multiple pieces of information from users (name, email, phone number, and preferred contact time) before submitting a service request. The conversation should handle interruptions and allow users to correct information. What Watson Assistant feature is best suited for this scenario?

A company is analyzing customer support tickets using Watson Natural Language Understanding to extract key topics and sentiment. They notice that the service identifies general sentiments but misses industry-specific negative indicators like 'system timeout' or 'data sync failed' which are critical issues for their product. How should they address this limitation?