WGU D685 PRACTICAL APPLICATIONS OF
PROMPT PA AND OA QUESTIONS AND
SOLUTIONS GRADED A+
◉ Narrow AI.
Answer: artificial intelligence that is designed and trained for a
specific task or narrow set of tasks
◉ General AI.
Answer: a hypothetical future AI system that would possess human-
level intelligence
◉ algorithms.
Answer: defined methods or processes employed to train models,
generate predictions, and execute tasks using data
◉ Machine Learning.
Answer: a branch of AI that enables computers to improve their
performance through experience without needing explicit
programming
◉ AI Model.
, Answer: a computer program designed to make predictions or
decisions based on input data
◉ Supervised Learning.
Answer: a technique where a model is trained using data that
includes labeled examples, such as images with tagged objects or
text with marked entities
◉ Unsupervised Learning.
Answer: a type of machine learning where the model is trained on
unlabeled data without explicit guidance or supervision
◉ Reinforcement Learning.
Answer: a type of machine learning wherein an AI agent learns
through interactions with an environment, garnering rewards or
penalties contingent upon its actions
◉ neural networks.
Answer: computational models inspired by the structure and
function of the human brain's neural networks that learn from data
called training to recognize patterns, make predictions, and perform
tasks such as classification, regression, and pattern recognition
◉ Deep Learning.
PROMPT PA AND OA QUESTIONS AND
SOLUTIONS GRADED A+
◉ Narrow AI.
Answer: artificial intelligence that is designed and trained for a
specific task or narrow set of tasks
◉ General AI.
Answer: a hypothetical future AI system that would possess human-
level intelligence
◉ algorithms.
Answer: defined methods or processes employed to train models,
generate predictions, and execute tasks using data
◉ Machine Learning.
Answer: a branch of AI that enables computers to improve their
performance through experience without needing explicit
programming
◉ AI Model.
, Answer: a computer program designed to make predictions or
decisions based on input data
◉ Supervised Learning.
Answer: a technique where a model is trained using data that
includes labeled examples, such as images with tagged objects or
text with marked entities
◉ Unsupervised Learning.
Answer: a type of machine learning where the model is trained on
unlabeled data without explicit guidance or supervision
◉ Reinforcement Learning.
Answer: a type of machine learning wherein an AI agent learns
through interactions with an environment, garnering rewards or
penalties contingent upon its actions
◉ neural networks.
Answer: computational models inspired by the structure and
function of the human brain's neural networks that learn from data
called training to recognize patterns, make predictions, and perform
tasks such as classification, regression, and pattern recognition
◉ Deep Learning.
