BUS 5114 Management Information Systems — Emerging
Technologies: AI, Big Data & IoT Bonus Quiz 2025
Course:
BUS 5114 Management Information Systems — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Emerging Technologies Bonus Quiz — 25 Q&A with Verified Answers
BUS 5114 Emerging Technologies: AI, Big Data, and IoT — Bonus
Practice Quiz
25 Questions with Verified Answers | Score: 96/100
Question 1:
Which of the following best describes the relationship between Artificial Intelligence (AI), Machine
Learning (ML), and Deep Learning (DL)?
A) They are three unrelated fields that sometimes overlap in business applications
B) AI is a subset of ML, which is a subset of DL
C) ML is a subset of AI, and DL is a subset of ML
D) DL and ML are interchangeable terms that together form the field of AI
Answer: C
Explanation:
Machine Learning is a subset of the broader field of Artificial Intelligence — it is the approach by
which systems learn from data rather than being explicitly programmed. Deep Learning is in turn a
subset of ML, specifically using multi-layered artificial neural networks to model complex patterns.
This nested relationship is foundational to understanding how modern AI systems are built.
Options A, B, and D misrepresent the hierarchy entirely.
, Question 2:
A retail bank trains a fraud detection model using millions of labeled historical transactions, each
marked as either "fraudulent" or "legitimate." Which type of machine learning is being used?
A) Unsupervised learning
B) Reinforcement learning
C) Supervised learning
D) Semi-supervised generative learning
Answer: C
Explanation:
Supervised learning uses labeled training data — where the correct output is already known — to
train a model to predict outcomes on new data. Because each transaction in this scenario is pre-
labeled as fraudulent or legitimate, this is a classic supervised learning problem. Unsupervised
learning works with unlabeled data to find hidden structure, while reinforcement learning involves
an agent learning through rewards and penalties in an environment.
Question 3:
A marketing analytics team groups their customers into segments based on purchasing patterns,
without any predefined categories or labels. Which machine learning paradigm best describes this
approach?
A) Supervised learning using a classification algorithm
B) Reinforcement learning with a reward-based environment
C) Transfer learning applied to an existing model
D) Unsupervised learning using clustering techniques
Answer: D
Explanation:
Unsupervised learning discovers patterns in data that has no predefined labels or categories.
Clustering — a core unsupervised technique — groups data points by similarity, which is exactly
what the marketing team is doing when segmenting customers by purchasing behavior.
Supervised learning would require pre-labeled segments. Reinforcement learning applies to
sequential decision-making tasks, not static segmentation. Transfer learning reuses a pre-trained
model on a new task.
Technologies: AI, Big Data & IoT Bonus Quiz 2025
Course:
BUS 5114 Management Information Systems — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Emerging Technologies Bonus Quiz — 25 Q&A with Verified Answers
BUS 5114 Emerging Technologies: AI, Big Data, and IoT — Bonus
Practice Quiz
25 Questions with Verified Answers | Score: 96/100
Question 1:
Which of the following best describes the relationship between Artificial Intelligence (AI), Machine
Learning (ML), and Deep Learning (DL)?
A) They are three unrelated fields that sometimes overlap in business applications
B) AI is a subset of ML, which is a subset of DL
C) ML is a subset of AI, and DL is a subset of ML
D) DL and ML are interchangeable terms that together form the field of AI
Answer: C
Explanation:
Machine Learning is a subset of the broader field of Artificial Intelligence — it is the approach by
which systems learn from data rather than being explicitly programmed. Deep Learning is in turn a
subset of ML, specifically using multi-layered artificial neural networks to model complex patterns.
This nested relationship is foundational to understanding how modern AI systems are built.
Options A, B, and D misrepresent the hierarchy entirely.
, Question 2:
A retail bank trains a fraud detection model using millions of labeled historical transactions, each
marked as either "fraudulent" or "legitimate." Which type of machine learning is being used?
A) Unsupervised learning
B) Reinforcement learning
C) Supervised learning
D) Semi-supervised generative learning
Answer: C
Explanation:
Supervised learning uses labeled training data — where the correct output is already known — to
train a model to predict outcomes on new data. Because each transaction in this scenario is pre-
labeled as fraudulent or legitimate, this is a classic supervised learning problem. Unsupervised
learning works with unlabeled data to find hidden structure, while reinforcement learning involves
an agent learning through rewards and penalties in an environment.
Question 3:
A marketing analytics team groups their customers into segments based on purchasing patterns,
without any predefined categories or labels. Which machine learning paradigm best describes this
approach?
A) Supervised learning using a classification algorithm
B) Reinforcement learning with a reward-based environment
C) Transfer learning applied to an existing model
D) Unsupervised learning using clustering techniques
Answer: D
Explanation:
Unsupervised learning discovers patterns in data that has no predefined labels or categories.
Clustering — a core unsupervised technique — groups data points by similarity, which is exactly
what the marketing team is doing when segmenting customers by purchasing behavior.
Supervised learning would require pre-labeled segments. Reinforcement learning applies to
sequential decision-making tasks, not static segmentation. Transfer learning reuses a pre-trained
model on a new task.
