50 IBM A1000-083 - Assessment: Foundations of Watson AI v2 Practice Questions: Question Bank 2025

A product team wants to quickly add an AI feature that classifies customer emails into categories (billing, technical support, cancelation) without building and hosting their own model infrastructure. Which approach best fits Watson AI services?

In supervised machine learning, which statement best describes the training data requirement?

A chatbot must determine whether a user is asking about store hours, order status, or returns. Which NLP concept is the chatbot primarily using to choose the correct response path?

An application calls a Watson AI model endpoint and receives a 401 Unauthorized response. What is the most likely cause?

A team built a churn prediction model. The dataset is 95% non-churn and 5% churn. Accuracy is 95%, but the model rarely detects churners. Which evaluation metric is most appropriate to focus on for the churn (positive) class?

A support assistant must extract specific fields from user messages such as account number, product name, and date of purchase. Which NLP capability is primarily required?

A team is designing an AI-powered claims processing workflow. They want a reusable pattern that separates the user interface, orchestration logic, and model inference so the model can be swapped without changing the UI. Which architecture best supports this goal?

During model training, the training accuracy is very high, but validation accuracy is much lower and remains low across epochs. What is the most likely issue, and what is a recommended mitigation?

A company wants to deploy an NLP model to help agents answer questions. They must explain to auditors which input terms most influenced each prediction, and they need this explanation per inference request. Which approach best supports this requirement?

A retrieval-augmented generation (RAG) assistant sometimes produces answers that contradict the latest policy documents. The documents are updated daily. What is the best troubleshooting step to address this issue first?

A support team wants to let business users build a chatbot that answers FAQs and can hand off to a human agent when it can’t help. Which Watson capability best fits this requirement?

A project team is building a supervised model to predict customer churn. Which statement best describes what they need to train the model?

You evaluate a binary classifier and find it achieves very high accuracy, but it misses many true positive cases that matter to the business. Which metric should you focus on improving?

A bank wants to quickly build a model from tabular data and compare multiple algorithms with minimal coding, while still being able to inspect the best model and its features. Which Watson Studio capability is most appropriate?

A team is designing an NLP pipeline to extract key information from customer emails (e.g., order number, product name, and issue type). Which approach best aligns with standard NLP concepts?

A model performs well during training but significantly worse on new, unseen data. Which issue is the most likely cause?

A developer exposes an AI scoring endpoint for a web application. Security requires that the client app never embeds long-lived credentials in the code. Which practice best meets this requirement?

A company wants users to ask natural-language questions over a large collection of PDFs and web pages. The system should return relevant passages and cite sources. Which Watson service is best suited for this retrieval-based requirement?

A team built a sentiment classifier from social media data. In production, performance degrades because new slang and topics appear. Which ongoing practice is most appropriate to address this problem?

A conversational bot frequently misroutes users because the same phrase (e.g., "I need help with my bill") matches multiple intents with similar confidence. What is the best design improvement to reduce misclassification while keeping the conversation natural?

A team wants to quickly add AI capabilities (speech, language, vision) into an existing application without building models from scratch. Which approach best matches IBM Watson AI services at a foundational level?

A product manager asks why a model that is 95% accurate might still be unacceptable for production. Which scenario best illustrates this risk?

A developer is building an NLP pipeline and needs to normalize text so that "Running", "runs", and "ran" are treated as the same base concept. Which technique is most appropriate?

A call center wants to route tickets by topic (billing, technical support, account cancellation). They have thousands of past tickets already labeled with the correct topic. Which machine learning approach is most suitable?

An NLP model performs well during evaluation but fails in production because users frequently use new slang and abbreviations not present in the training data. What is the most likely underlying issue?

A company is training a binary classifier and sees the following results: Precision is high, but recall is very low. Which adjustment most directly targets improving recall?

A team is building a chatbot and wants it to handle user questions even when phrased in many different ways (paraphrases). Which design choice best supports this requirement?

A healthcare application must provide explanations for predictions to meet internal governance requirements. Which practice best addresses this need in an AI solution lifecycle?

A sentiment analysis model shows strong performance on movie reviews but performs poorly on customer support chats. What is the most appropriate next step before retraining?

A team integrates multiple Watson-based AI components into an application: speech-to-text, NLP enrichment, and a downstream classifier. They need to troubleshoot intermittent failures and latency spikes end-to-end. Which architecture practice most directly improves observability for this pipeline?

A customer support team wants to quickly identify whether incoming chat messages are positive, negative, or neutral to help prioritize escalations. Which Watson capability best fits this need?

A data scientist is building a classifier and wants to estimate how well it will generalize to new, unseen data before deploying it. Which approach is most appropriate?

A developer wants to build an AI-powered application that can be integrated into a web portal and accessed via REST APIs. Which high-level pattern is most appropriate?

Which statement best describes an IBM Watson AI service in the context of building AI solutions?

A team observes that their classification model has very high training accuracy but significantly lower validation accuracy. Which issue is most likely occurring, and what is a common mitigation?

A company wants to automatically extract key fields (such as invoice number, due date, and total amount) from a variety of invoice formats. The invoices may be scanned and contain both printed text and tabular layouts. Which approach is most appropriate?

A chatbot must route user messages to one of several departments (Billing, Technical Support, Sales). Messages are short and may contain typos. What is the most suitable NLP task to implement for routing?

An application uses an external Watson AI endpoint. In production, some requests intermittently fail due to transient network issues. Which implementation practice best improves reliability without changing the AI model?

A model is trained to detect fraudulent transactions where only 1% of transactions are fraud. The model achieves 99% accuracy by predicting 'not fraud' for every case. Which metric is most appropriate to evaluate whether the model is actually useful for fraud detection?

A team fine-tunes a language model for internal use. They notice the model occasionally reproduces sensitive training snippets verbatim when prompted with certain phrases. What is the most appropriate mitigation strategy?

A team is new to IBM Watson AI and wants a high-level way to compare available Watson AI services and understand typical use cases before building anything. What is the BEST first step?

A data scientist notices their classification model has 96% accuracy, but the dataset has 96% of records in a single class. Which metric would BEST reflect how well the model performs across classes?

An NLP pipeline must treat "IBM", "I.B.M.", and "ibm" as the same entity where appropriate. Which preprocessing step BEST supports this goal?

A team needs a supervised learning approach to predict monthly energy consumption (a numeric value) for buildings using features like square footage, insulation rating, and average temperature. Which model type is MOST appropriate?

A conversational assistant frequently fails to recognize user intent when users include slang and typos. What is the MOST effective improvement to increase intent recognition robustness?

An application integrates an AI service through an API. In production, responses intermittently fail due to transient network timeouts. Which approach is the BEST practice to improve reliability without changing the AI model?

A model performs well during training but poorly on new, unseen data. The training loss is low, and the validation loss is significantly higher. What is the MOST likely issue?

A team is building a document search feature that must return an answer with supporting passages from a large internal knowledge base. Users want citations and the ability to trace the source text. Which solution pattern is MOST appropriate?

A team is training a model to screen job applicants and discovers it disproportionately rejects candidates from a protected group. They want to reduce unfair outcomes while maintaining acceptable performance. What is the BEST next step?

You deploy an NLP model and later observe a gradual drop in performance as new customer terminology and products appear. There is no infrastructure issue. Which explanation and response is MOST appropriate?