IBM Assessment: Cognitive Solutions V1 Intermediate Practice Exam: Medium Difficulty 2025

A healthcare organization is building a chatbot to help patients schedule appointments and answer basic medical questions. The chatbot needs to understand patient intent, extract relevant entities like appointment dates and symptoms, and provide accurate responses from a knowledge base. Which combination of Watson services would be most appropriate for this solution?

A retail company wants to analyze customer feedback from multiple sources including social media, email, and call center transcripts to identify sentiment trends and emerging issues. They need to process unstructured text and categorize feedback by product line. What is the most effective approach using Watson services?

During the implementation of a Watson Assistant solution, a developer notices that the assistant frequently misclassifies similar user intents such as 'cancel my order' and 'modify my order'. Both intents have sufficient training examples. What is the best approach to resolve this issue?

An enterprise is designing a cognitive solution that will process sensitive customer financial data using Watson services. The solution must comply with data privacy regulations and maintain data residency within specific geographic regions. Which architectural considerations are most critical?

A Watson Discovery collection is returning too many irrelevant results for user queries. The collection contains 10,000 technical documents with specialized terminology. What combination of techniques would best improve result relevance?

A development team is integrating Watson Natural Language Understanding into an application that analyzes news articles. They need to extract custom entities specific to their industry (e.g., proprietary product names, internal company codes) that are not recognized by the default model. What is the appropriate solution?

A Watson Assistant is being deployed to handle customer service inquiries across multiple channels (web chat, mobile app, and phone). The assistant needs to maintain conversation context across channels and hand off to human agents when necessary. Which design pattern best supports these requirements?

During load testing of a Watson-based application, the team notices increased latency and occasional timeout errors when multiple API calls are made simultaneously. The application makes sequential calls to Watson Natural Language Understanding and Watson Discovery for each user request. How should this be optimized?

A company is implementing a Watson Discovery solution to search internal HR documents including employee handbooks, policy documents, and benefits guides. Some documents contain sensitive information that should only be accessible to specific user groups. What is the recommended approach to implement document-level security?

A Watson Assistant implementation needs to collect user information (name, email, account number) during a conversation, validate the input format, and handle cases where users provide invalid data. The assistant should re-prompt users for corrections. What is the most efficient way to implement this pattern?