WGU D685 Objective Assessment Study guide | 2026/2027
Update | Practical Applications of Prompt Engineering | Verified
Questions & Answers | Grade A
The limitations of AI can be categorized into three main areas:
- fundamental limitations of AI
- practical limitations and challenges
- societal concerns and implications.
Fundamental limitations of AI include:
- dependence on training data
- limited common sense
- lack of emotional sense.
Practical limitations of AI include:
- perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
Societal concerns on AI include:
- data privacy
- safety
- security concerns
,artificial intelligence (AI)
the study of creating machines and computer systems capable of performing tasks that
typically require human intelligence
narrow AI
artificial intelligence that is designed and trained for a specific task or narrow set of
tasks
general AI
a hypothetical future AI system that would possess human-level intelligence
algorithms
defined methods or processes employed to train models, generate predictions, and
execute tasks using data
machine learning
a branch of AI that enables computers to improve their performance through experience
without needing explicit programming
AI model
a computer program designed to make predictions or decisions based on input data
supervised learning
,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
a type of machine learning where the model is trained on unlabeled data without explicit
guidance or supervision
reinforcement learning
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
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
a powerful subset of machine learning that uses artificial neural networks to learn from
large amounts of data
generative AI
AI systems that can create new content
, large language models (LLMs)
a type of machine learning model that is trained on massive amounts of text data to
understand and generate human-like language
natural language processing (NLP)
the field of AI concentrated on enabling computers to understand and engage with
human language, mirroring the intricacies of human communication
chatbots
AI programs designed to engage in natural conversations with people, providing
information, answering questions, and even offering emotional support
computer vision
a field of artificial intelligence that enables computers to interpret and analyze visual
information from the real world, such as images and videos
robotics
the field of AI that focuses on designing, constructing, and operating robots
statistical analysis
a technique employed in AI that involves collecting, organizing, examining, and
interpreting data to identify patterns and make predictions
Update | Practical Applications of Prompt Engineering | Verified
Questions & Answers | Grade A
The limitations of AI can be categorized into three main areas:
- fundamental limitations of AI
- practical limitations and challenges
- societal concerns and implications.
Fundamental limitations of AI include:
- dependence on training data
- limited common sense
- lack of emotional sense.
Practical limitations of AI include:
- perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
Societal concerns on AI include:
- data privacy
- safety
- security concerns
,artificial intelligence (AI)
the study of creating machines and computer systems capable of performing tasks that
typically require human intelligence
narrow AI
artificial intelligence that is designed and trained for a specific task or narrow set of
tasks
general AI
a hypothetical future AI system that would possess human-level intelligence
algorithms
defined methods or processes employed to train models, generate predictions, and
execute tasks using data
machine learning
a branch of AI that enables computers to improve their performance through experience
without needing explicit programming
AI model
a computer program designed to make predictions or decisions based on input data
supervised learning
,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
a type of machine learning where the model is trained on unlabeled data without explicit
guidance or supervision
reinforcement learning
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
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
a powerful subset of machine learning that uses artificial neural networks to learn from
large amounts of data
generative AI
AI systems that can create new content
, large language models (LLMs)
a type of machine learning model that is trained on massive amounts of text data to
understand and generate human-like language
natural language processing (NLP)
the field of AI concentrated on enabling computers to understand and engage with
human language, mirroring the intricacies of human communication
chatbots
AI programs designed to engage in natural conversations with people, providing
information, answering questions, and even offering emotional support
computer vision
a field of artificial intelligence that enables computers to interpret and analyze visual
information from the real world, such as images and videos
robotics
the field of AI that focuses on designing, constructing, and operating robots
statistical analysis
a technique employed in AI that involves collecting, organizing, examining, and
interpreting data to identify patterns and make predictions
