1|Page
WGU D685 - PRACTICAL APPLICATIONS OF
PROMPT NEWEST EXAM 2026 | ALL QUESTIONS
AND CORRECT DETAILED ANSWERS | ALREADY A
GRADED | NEW AND REVISED
1. The limitations of AI can be categorized into three main areas:: - fundamental limitations
of AI
- practical limitations and challenges
- societal concerns and implications.
2. Fundamental limitations of AI include:: - dependence on training data
- limited common sense
- lack of emotional sense.
3. Practical limitations of AI include:: - perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
4. Societal concerns on AI include:: - data privacy
- safety
- security concerns
5. artificial intelligence (AI): the study of creating machines and computer systems capable of performing tasks that
typically require human intelligence
6. narrow AI: artificial intelligence that is designed and trained for a specific task or narrow set of tasks
7. general AI: a hypothetical future AI system that would possess human-level intelligence
8. algorithms: defined methods or processes employed to train models, generate predictions, and execute tasks using data
9. machine learning: a branch of AI that enables computers to improve their performance through experience without
needing explicit programming
10. AI model: a computer program designed to make predictions or decisions based on input data
11. 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
12. unsupervised learning: a type of machine learning where the model is trained on unlabeled data without
explicit guidance or supervision
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13. 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
14. 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
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15. deep learning: a powerful subset of machine learning that uses artificial neural networks to learn from large amounts of
data
16. generative AI: AI systems that can create new content
17. 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
18. 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
19. chatbots: AI programs designed to engage in natural conversations with people, providing information, answering
questions, and even ottering emotional support
20. 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
21. robotics: the field of AI that focuses on designing, constructing, and operating robots
22. statistical analysis: a technique employed in AI that involves collecting, organizing, examining, and interpreting
data to identify patterns and make predictions
23. AI tools: software programs designed to assist users in performing AI-related tasks
24. This is an example of Few-Shot Prompting: Match the example prompts with the technique: "Write a
dialogue between two friends planning a trip."
25. structured data: data that is organized in a well-defined format and is typically stored in databases or spreadsheets
26. unstructured data: includes text documents, images, videos, audio recordings, social media posts, and other types of
data that do not fit neatly into a structured format
27. byte: a unit of digital information
28. gigabyte (GB): a unit of digital information equal to approximately one billion bytes
29. terabyte (TB): a unit of digital information equal to approximately one trillion bytes
30. hallucinations: an incorrect, misleading, nonsensical, or entirely fabricated output generated by an AI model
31. adversarial attack: deliberate attempts to deceive or manipulate AI systems by introducing carefully crafted perturbations
to the input data
32. AI drift: the phenomenon where the performance of an AI model deteriorates over time as the underlying data distribution
changes
33. generative adversarial networks (GANs): a type of deep learning model that uses an adversarial training
process to create new data
34. prompt: a user query, command, or input in an AI interface
WGU D685 - PRACTICAL APPLICATIONS OF
PROMPT NEWEST EXAM 2026 | ALL QUESTIONS
AND CORRECT DETAILED ANSWERS | ALREADY A
GRADED | NEW AND REVISED
1. The limitations of AI can be categorized into three main areas:: - fundamental limitations
of AI
- practical limitations and challenges
- societal concerns and implications.
2. Fundamental limitations of AI include:: - dependence on training data
- limited common sense
- lack of emotional sense.
3. Practical limitations of AI include:: - perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
4. Societal concerns on AI include:: - data privacy
- safety
- security concerns
5. artificial intelligence (AI): the study of creating machines and computer systems capable of performing tasks that
typically require human intelligence
6. narrow AI: artificial intelligence that is designed and trained for a specific task or narrow set of tasks
7. general AI: a hypothetical future AI system that would possess human-level intelligence
8. algorithms: defined methods or processes employed to train models, generate predictions, and execute tasks using data
9. machine learning: a branch of AI that enables computers to improve their performance through experience without
needing explicit programming
10. AI model: a computer program designed to make predictions or decisions based on input data
11. 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
12. unsupervised learning: a type of machine learning where the model is trained on unlabeled data without
explicit guidance or supervision
,2|Page
13. 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
14. 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
,3|Page
15. deep learning: a powerful subset of machine learning that uses artificial neural networks to learn from large amounts of
data
16. generative AI: AI systems that can create new content
17. 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
18. 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
19. chatbots: AI programs designed to engage in natural conversations with people, providing information, answering
questions, and even ottering emotional support
20. 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
21. robotics: the field of AI that focuses on designing, constructing, and operating robots
22. statistical analysis: a technique employed in AI that involves collecting, organizing, examining, and interpreting
data to identify patterns and make predictions
23. AI tools: software programs designed to assist users in performing AI-related tasks
24. This is an example of Few-Shot Prompting: Match the example prompts with the technique: "Write a
dialogue between two friends planning a trip."
25. structured data: data that is organized in a well-defined format and is typically stored in databases or spreadsheets
26. unstructured data: includes text documents, images, videos, audio recordings, social media posts, and other types of
data that do not fit neatly into a structured format
27. byte: a unit of digital information
28. gigabyte (GB): a unit of digital information equal to approximately one billion bytes
29. terabyte (TB): a unit of digital information equal to approximately one trillion bytes
30. hallucinations: an incorrect, misleading, nonsensical, or entirely fabricated output generated by an AI model
31. adversarial attack: deliberate attempts to deceive or manipulate AI systems by introducing carefully crafted perturbations
to the input data
32. AI drift: the phenomenon where the performance of an AI model deteriorates over time as the underlying data distribution
changes
33. generative adversarial networks (GANs): a type of deep learning model that uses an adversarial training
process to create new data
34. prompt: a user query, command, or input in an AI interface
