WGU D423 – Artificial Intelligence
Foundations Final Prep Exam (Latest
Update 2026/2027) Questions and
Verified Answers | 100% Correct |
Grade A.
1. Which statement best describes artificial intelligence (AI)?
A. A programming language used to build websites
B. A field of computer science focused on creating systems that
perform tasks requiring human intelligence
C. A type of computer hardware used in networking
D. A database management system
Rationale: Artificial intelligence is the branch of computer science
dedicated to developing systems capable of performing tasks such as
learning, reasoning, problem-solving, perception, and language
understanding. AI includes many subfields such as machine learning,
natural language processing, computer vision, robotics, and expert
systems.
2. Which subset of AI enables computers to improve performance by
learning from data without being explicitly programmed for every
task?
,A. Robotics
B. Expert systems
C. Machine learning
D. Virtualization
Rationale: Machine learning is a subset of AI that allows algorithms to
identify patterns from data and improve predictions or decisions over
time. Instead of relying entirely on manually written rules, machine
learning models learn relationships directly from examples.
3. Which machine learning approach uses labeled training data?
A. Reinforcement learning
B. Supervised learning
C. Unsupervised learning
D. Evolutionary learning
Rationale: Supervised learning relies on labeled datasets where each
input has a known output. The algorithm learns the mapping between
inputs and outputs to make predictions on new, unseen data.
4. Which machine learning technique is primarily used to discover
hidden patterns in unlabeled data?
A. Regression
B. Classification
C. Clustering
D. Reinforcement learning
,Rationale: Clustering is an unsupervised learning technique that groups
similar data points together without predefined labels. It is commonly
used for customer segmentation, anomaly detection, and exploratory
data analysis.
5. Which AI application is primarily responsible for translating speech
into text?
A. Computer vision
B. Robotics
C. Natural language processing (NLP)
D. Expert systems
Rationale: Natural language processing enables computers to
understand, interpret, generate, and process human language. Speech
recognition systems often combine NLP with audio signal processing to
convert spoken language into text.
6. What is the primary goal of supervised learning?
A. Generate random outputs
B. Discover hidden clusters only
C. Predict correct outputs from labeled examples
D. Replace human programmers
Rationale: Supervised learning seeks to learn relationships between
inputs and labeled outputs so that the model can accurately predict
future outcomes. Common supervised learning tasks include
classification and regression.
, 7. Which algorithm is commonly used for classification problems?
A. K-Means
B. Apriori
C. Decision Tree
D. Principal Component Analysis
Rationale: Decision trees classify data by creating branching structures
based on feature values. They are easy to interpret and widely used for
predictive modeling and decision support.
8. What does overfitting occur when?
A. The dataset is too small to train
B. A model memorizes training data and performs poorly on new data
C. The computer runs out of storage
D. The model contains too few features
Rationale: Overfitting happens when a model captures noise and
specific details in the training data instead of general patterns. While
training accuracy may be high, performance on unseen data suffers.
9. Which metric is commonly used to evaluate classification models?
A. Mean Squared Error
B. Accuracy
C. Variance
D. Covariance
Foundations Final Prep Exam (Latest
Update 2026/2027) Questions and
Verified Answers | 100% Correct |
Grade A.
1. Which statement best describes artificial intelligence (AI)?
A. A programming language used to build websites
B. A field of computer science focused on creating systems that
perform tasks requiring human intelligence
C. A type of computer hardware used in networking
D. A database management system
Rationale: Artificial intelligence is the branch of computer science
dedicated to developing systems capable of performing tasks such as
learning, reasoning, problem-solving, perception, and language
understanding. AI includes many subfields such as machine learning,
natural language processing, computer vision, robotics, and expert
systems.
2. Which subset of AI enables computers to improve performance by
learning from data without being explicitly programmed for every
task?
,A. Robotics
B. Expert systems
C. Machine learning
D. Virtualization
Rationale: Machine learning is a subset of AI that allows algorithms to
identify patterns from data and improve predictions or decisions over
time. Instead of relying entirely on manually written rules, machine
learning models learn relationships directly from examples.
3. Which machine learning approach uses labeled training data?
A. Reinforcement learning
B. Supervised learning
C. Unsupervised learning
D. Evolutionary learning
Rationale: Supervised learning relies on labeled datasets where each
input has a known output. The algorithm learns the mapping between
inputs and outputs to make predictions on new, unseen data.
4. Which machine learning technique is primarily used to discover
hidden patterns in unlabeled data?
A. Regression
B. Classification
C. Clustering
D. Reinforcement learning
,Rationale: Clustering is an unsupervised learning technique that groups
similar data points together without predefined labels. It is commonly
used for customer segmentation, anomaly detection, and exploratory
data analysis.
5. Which AI application is primarily responsible for translating speech
into text?
A. Computer vision
B. Robotics
C. Natural language processing (NLP)
D. Expert systems
Rationale: Natural language processing enables computers to
understand, interpret, generate, and process human language. Speech
recognition systems often combine NLP with audio signal processing to
convert spoken language into text.
6. What is the primary goal of supervised learning?
A. Generate random outputs
B. Discover hidden clusters only
C. Predict correct outputs from labeled examples
D. Replace human programmers
Rationale: Supervised learning seeks to learn relationships between
inputs and labeled outputs so that the model can accurately predict
future outcomes. Common supervised learning tasks include
classification and regression.
, 7. Which algorithm is commonly used for classification problems?
A. K-Means
B. Apriori
C. Decision Tree
D. Principal Component Analysis
Rationale: Decision trees classify data by creating branching structures
based on feature values. They are easy to interpret and widely used for
predictive modeling and decision support.
8. What does overfitting occur when?
A. The dataset is too small to train
B. A model memorizes training data and performs poorly on new data
C. The computer runs out of storage
D. The model contains too few features
Rationale: Overfitting happens when a model captures noise and
specific details in the training data instead of general patterns. While
training accuracy may be high, performance on unseen data suffers.
9. Which metric is commonly used to evaluate classification models?
A. Mean Squared Error
B. Accuracy
C. Variance
D. Covariance
