IBM A1000-083 - Assessment: Foundations of Watson AI v2 Practice Exam 2025: Latest Questions

A business analyst wants to quickly identify themes and sentiment in thousands of open-ended survey comments without building a custom model. Which Watson AI service capability best fits this need?

A team is new to machine learning. They ask what the primary difference is between supervised and unsupervised learning. Which statement is correct?

An application needs to convert a live customer support call into text in near real time to enable downstream analytics. Which Watson AI capability is most appropriate?

A product owner asks why an ML model performs very well on training data but poorly on new, unseen data. What is the most likely issue?

A team is building a text classification model and notices that one class makes up 95% of the training dataset. Accuracy is high, but minority-class predictions are poor. Which metric is most appropriate to evaluate performance in this situation?

A support chatbot must keep track of context across multiple turns (e.g., the user first provides an order number, then asks to change the delivery address). What is the recommended design approach?

A developer integrates an NLP service but receives inconsistent results for entity extraction. Investigation shows user text sometimes includes extra spaces, mixed casing, and common typos. What is the best first step to improve consistency?

A data scientist wants to deploy a trained model so multiple applications can call it via a stable endpoint, with access control and versioned deployments. Which approach best matches this requirement?

A bank must explain model decisions for loan approvals to comply with internal governance and external audits. Which practice best supports this requirement while developing and deploying the model?

A team builds a production NLP pipeline. After deployment, they observe that the language mix of incoming text shifts significantly (more messages in a different dialect and more slang), causing a drop in intent classification quality. What is the most appropriate mitigation strategy?

A product team wants to quickly determine whether incoming support messages express frustration, satisfaction, or neutrality so they can prioritize follow-ups. Which Watson AI capability best fits this need?

A data scientist notices a classification model performs extremely well on the training set but significantly worse on new, unseen data. What is the most likely issue?

A developer wants an application to call a Watson AI service without embedding long-term credentials in the mobile app. Which approach is the best practice?

A team is building a model to predict whether a customer will churn (yes/no). The dataset contains 95% non-churn and 5% churn. Which evaluation metric is most appropriate to understand performance on the minority class?

A customer service chatbot must identify a user's intent even when they use varied wording (e.g., "reset my password", "can't log in", "forgot credentials"). Which NLP concept directly supports this requirement?

A team wants to improve a Watson-based assistant by using conversation logs to identify where users get stuck (e.g., abandon a flow or repeat questions). What is the best next step?

A company wants an architecture where an application sends text to an AI service, stores results for later auditing, and ensures the AI call does not block the user interface. Which design is most appropriate?

During text classification, a model performs poorly after deployment because real user messages include many acronyms and domain-specific terms not present in the training data. What is the best remediation?

A financial institution wants to use an AI service to extract key fields from customer-submitted documents and must be able to explain how the extracted values were obtained for audit purposes. Which approach best supports this requirement?

A team deploys a conversational AI solution and discovers the assistant frequently selects the wrong intent between two very similar intents (e.g., "change address" vs "change shipping address"). What is the most effective corrective action?

A retail team is building a chatbot and wants users to ask questions like “Where is my order?” and “What’s your return policy?” They need the system to identify the user’s goal and extract key details (for example, an order number) from the message. Which Watson capability best fits this requirement?

A data scientist trains a binary classification model to detect fraudulent transactions. Only 1% of transactions are fraudulent. The model achieves 99% accuracy but fails to catch most fraud cases. Which metric is most appropriate to prioritize to better reflect performance on the minority class?

A team is evaluating multiple ML models. They observe excellent training performance but significantly worse validation performance. Which issue is most likely occurring, and what is the best corrective action?

A bank wants to build an application that answers employee questions using internal policy documents (PDFs, web pages, and manuals). The app must return answers grounded in those documents and provide the source passages for auditability. Which architecture is the most appropriate?

A developer deploys an AI-powered web app that calls a Watson service API. In production, requests intermittently fail with authentication errors after the app has been running for several hours. The API key is stored correctly and works again after a manual restart. What is the most likely root cause and best practice fix?