Artificial Intelligence & Decision Support Systems (107 Exam Questions) – DSS, Expert Systems, Machine Learning, EIS & Intelligent Agents | MIS 2026 Exam

This comprehensive Management Information Systems (MIS) review guide contains 107 exam-style questions and detailed answers covering the core principles of Artificial Intelligence (AI), Decision Support Systems (DSS), Executive Information Systems (EIS), Expert Systems, and intelligent decision-making technologies used in modern organizations. Designed to support university-level information systems courses and professional certification preparation, the material provides an in-depth exploration of how organizations leverage AI technologies to improve problem-solving, automate decision processes, enhance strategic planning, and create competitive advantages in data-driven environments. The question-and-answer format promotes active recall, reinforces conceptual understanding, and prepares students for examination scenarios requiring both theoretical knowledge and practical application. The guide begins with foundational concepts in Artificial Intelligence, defining AI as technologies that simulate human thought processes through capabilities such as reasoning, perception, knowledge representation, learning, understanding, and cognitive processing. Students explore major AI applications across industries, including fraud detection in taxation, predictive marketing analytics, healthcare decision support, airline maintenance scheduling, energy exploration, robotics, and welfare eligibility systems. The material highlights how organizations increasingly integrate AI into operational and strategic activities to improve efficiency, accuracy, and decision quality. A substantial portion of the review focuses on Expert Systems and knowledge-based technologies. Learners examine the architecture of expert systems, including knowledge acquisition facilities, knowledge bases, inference engines, explanation facilities, user interfaces, and Knowledge Base Management Systems (KBMS). The document further explores heuristic reasoning, forward chaining, backward chaining, case-based reasoning (CBR), and the Four Rs of CBR—Retrieve, Reuse, Revise, and Retain. Students gain insight into when expert systems should and should not be implemented, along with their advantages, limitations, and applications across industries such as banking, agriculture, education, government, and forensic science. The material also provides extensive coverage of Machine Learning, Artificial Neural Networks (ANNs), Natural Language Processing (NLP), Genetic Algorithms, Fuzzy Logic, Intelligent Agents, and Robotic Process Automation (RPA). Students learn how neural networks solve poorly structured problems, how machine learning enables systems to improve through experience, and how NLP facilitates human-computer interaction using natural language. Additional topics include intelligent agents and their characteristics, contextual computing, data mining agents, robotic technologies, optimization through genetic algorithms, and the emerging ethical concerns surrounding automation and workforce displacement. Decision-making frameworks form another major focus of this study resource. Students examine Herbert Simon's four phases of decision-making—Intelligence, Design, Choice, and Implementation—while distinguishing between structured, semistructured, and unstructured decisions. The guide thoroughly explains Decision Support Systems (DSS), including their components, capabilities, design requirements, roles of managerial and technical designers, model builders, and analytical techniques such as what-if analysis, goal-seeking analysis, sensitivity analysis, optimization analysis, and exception reporting. Concepts related to Executive Information Systems (EIS), Geographic Information Systems (GIS), collaboration technologies, digital dashboards, and integrated decision support systems (IDSSs) further strengthen students' understanding of managerial decision support technologies. The final sections address contemporary issues involving AI ethics, governance, and implementation. Topics such as algorithmic bias, AI errors, wealth inequality, unemployment concerns, organizational readiness, ethical frameworks, and the societal implications of intelligent technologies encourage learners to critically evaluate both the opportunities and challenges associated with widespread AI adoption. The material reflects the growing emphasis placed on responsible innovation and ethical leadership within business and technology education. The concepts presented align closely with undergraduate and graduate curricula in Management Information Systems, Business Analytics, Information Technology Management, Artificial Intelligence in Business, and Decision Sciences. This resource serves as a valuable review tool for reinforcing exam readiness while developing a practical understanding of intelligent systems and managerial decision-making technologies. Referenced Academic Sources: • Laudon KC, Laudon JP. Management Information Systems: Managing the Digital Firm. Pearson Education. • Baltzan P. Business Driven Information Systems. McGraw-Hill Education. • Turban E, Sharda R, Delen D. Decision Support and Business Intelligence Systems. Pearson. • Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. Pearson. • Nilsson NJ. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press. • Simon HA. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations. Free Press. • Davenport TH, Ronanki R. Artificial Intelligence for the Real World. Harvard Business Review. • Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press. Relevant Students: This document is highly recommended for Management Information Systems (MIS) students, Business Analytics students, Information Systems students, Computer Information Systems learners, Information Technology Management students, Decision Sciences students, Data Analytics students, Artificial Intelligence in Business students, Business Administration majors, MBA candidates, Business Intelligence learners, digital transformation professionals, and students preparing for university examinations, certification assessments, or comprehensive reviews involving AI-enabled decision support technologies. Keywords: Artificial Intelligence, Decision Support Systems, DSS, MIS Exam Questions, Management Information Systems, AI Exam Review, Expert Systems, Knowledge Base, Inference Engine, Knowledge Acquisition, Explanation Facility, Knowledge Base Management System, KBMS, Machine Learning, Artificial Neural Networks, ANNs, Neural Networks, Natural Language Processing, NLP, Genetic Algorithms, Fuzzy Logic, Intelligent Agents, Virtual Agents, Bots, Robotic Process Automation, RPA, Case Based Reasoning, CBR, Forward Chaining, Backward Chaining, Heuristic Reasoning, Executive Information Systems, EIS, Geographic Information Systems, GIS, Digital Dashboards, Structured Decisions, Semistructured Decisions, Unstructured Decisions, Herbert Simon Decision Making, What If Analysis, Goal Seeking Analysis, Sensitivity Analysis, Optimization Analysis, Exception Reporting, Collaboration Systems, Data Mining Agents, Contextual Computing, AI Ethics, Algorithmic Bias, Decision Making Technologies, Business Intelligence, Intelligent Decision Support Systems, MIS Study Guide, Business Technology Exam Preparation