IBM A1000-119 Practice Exam 2025: Latest Questions

A team is explaining AI to business stakeholders. Which statement best describes the relationship between AI, machine learning (ML), and deep learning (DL)?

A retailer wants to predict whether a customer will churn (yes/no) using historical customer behavior data with known churn outcomes. What type of machine learning problem is this?

A support center wants to reduce average handle time by automatically answering common questions using an AI system. Which solution is the most appropriate?

An organization wants to use customer data to train an AI model. Which action best aligns with responsible AI and privacy best practices?

A data scientist notices a binary classifier achieves 95% accuracy, but most customers are non-churners (highly imbalanced classes). Which evaluation approach is most appropriate to understand model performance?

A team is building a document processing solution that extracts key fields (invoice number, total amount, vendor name) from semi-structured invoices. Which AI capability is the best fit?

A model performs well in testing but degrades after deployment as customer behavior changes. Which concept best describes this issue, and what is a common response?

An organization plans to deploy an AI model for loan approvals. Which governance practice best supports auditability and accountability?

A healthcare provider wants to train a model using data from multiple hospitals, but regulations prevent sharing raw patient data across sites. Which approach best addresses this constraint while still enabling learning from distributed data?

A company is building an AI system to assist agents by recommending next-best actions during live customer calls. The system must provide low-latency responses and still allow periodic model updates. Which architecture choice best meets these needs?

A team is explaining AI to business stakeholders. Which statement best distinguishes artificial intelligence (AI) from machine learning (ML)?

A product owner wants a model that assigns each customer to one of three churn risk categories: Low, Medium, High. Which type of machine learning problem is this?

A data science team evaluates a binary classifier for a rare disease screening program (1% positive rate). Which metric is typically most appropriate to emphasize to minimize missed true cases?

A retailer wants to recommend products based on a customer's previous browsing and purchase behavior. Which AI approach best fits this use case?

During model evaluation, the team notices training accuracy is very high but validation accuracy is much lower. What is the most likely issue?

A bank wants to deploy an AI model for credit decisions. Regulators require the bank to explain to applicants which factors influenced a denial. Which is the most appropriate design choice?

A support chatbot frequently produces confident-sounding but incorrect answers. Which mitigation is most aligned with responsible AI practices for deployed generative systems?

A project team wants to reduce bias in a hiring-screening model. Which action is most appropriate during the data and modeling phase?

A model was trained on last year's consumer behavior data. After deployment, conversion rates drop sharply even though infrastructure is stable. A review shows customer preferences shifted due to market changes. What is the most likely cause and best next step?

An enterprise wants to operationalize an ML model with strong governance. They need repeatable deployments, traceability from dataset to model to prediction, and the ability to roll back. Which architecture pattern best meets these needs?

A data scientist evaluates a classifier on a dataset where only 2% of cases are positive (fraud). The model achieves 98% accuracy but misses most fraud cases. Which evaluation metric is MOST appropriate to better understand performance on the positive class?

A team wants to deploy an AI assistant that can answer questions using both internal policy documents and a large language model. They need the assistant to reference up-to-date documents without retraining the model. Which architecture pattern BEST fits this requirement?

A retailer notices that a demand-forecasting model performed well during training but performs poorly after deployment because customer behavior has changed over time. What is the MOST likely issue?

A healthcare organization wants to use AI to assist with triage, but must ensure decisions can be explained to clinicians and auditors. Which approach is the BEST fit for foundational, high-stakes explainability requirements?

A company is building a customer-facing generative AI chatbot. During testing, it occasionally invents product policies that are not in the official documentation. Which combination of controls BEST reduces this risk while supporting governance expectations?