1|Page
WGU D685 - PRACTICAL APPLICATIONS OF PROMPT OA
AND PA | FREQUENTLY TESTED QUESTIONS WITH
CORRECT ANSWERS | BRAND NEW!
Consistency in AI responses builds user trust and encourages continued
use of AI services. ......ANSWER......Why is consistency important in AI-
generated responses?
Effective prompts are clear, structured, and detailed, including modifiers
and iterative refinement for improved AI output. ......ANSWER......What
characteristics define successful AI prompts?
Scenario-based prompts with specificity and clarity produce more
relevant and engaging AI-generated text. ......ANSWER......Why is
specificity important in prompts for AI text generation scenarios?
Few-shot learning uses examples to guide AI, while zero-shot relies on
pre-existing knowledge without examples. ......ANSWER......How do few-
shot and zero-shot prompting techniques differ in guiding AI responses?
Tree-of-thought prompting helps AI explore multiple solutions before
selecting the best one. ......ANSWER......Which prompting technique
encourages AI to evaluate multiple reasoning paths before responding?
,2|Page
Sampling bias occurs when AI training data does not represent the
entire population accurately, leading to skewed outcomes.
......ANSWER......What is sampling bias in AI?
Measurement bias arises from errors or inaccuracies in data collection,
such as leading survey questions. ......ANSWER......Which scenario
exemplifies measurement bias?
Data bias can create feedback loops where AI responses reinforce
existing user viewpoints, limiting perspective diversity.
......ANSWER......How can repeated prompting lead to bias
reinforcement in AI systems?
User confirmation bias leads AI to reinforce users' existing beliefs,
limiting exposure to diverse perspectives. ......ANSWER......What is user
confirmation bias in AI interactions?
Algorithmic bias arises from assumptions in AI design that can amplify
existing data biases. ......ANSWER......What is algorithmic bias and how
does it affect AI outcomes?
Mitigating bias involves diverse data, bias detection tools, transparency,
and continuous monitoring. ......ANSWER......What strategies help
mitigate data bias in AI systems?
,3|Page
Bias and fairness, transparency and accountability, privacy, autonomy
and control, employment impact, and moral decision-making.
......ANSWER......What are the key ethical areas to consider when
evaluating AI systems?
Explainable AI (XAI) provides understandable justifications for AI
decisions and supports accountability. ......ANSWER......Why is
transparency (or explainability) important in AI systems?
Autonomy in AI (e.g., self-driving cars) raises questions about human
oversight and who is responsible for decisions made by autonomous
systems. ......ANSWER......What ethical issues arise from deploying
autonomous AI systems?
Extensive data collection, insecure storage, poor anonymization, and
inference of sensitive traits can all threaten individual privacy.
......ANSWER......What are the main privacy risks associated with AI data
practices?
Federated learning, strong encryption, privacy-by-design, data
minimization, and regular audits help protect user data and privacy.
......ANSWER......Which practical strategies reduce privacy risks when
developing and deploying AI systems?
AI hallucinations are false or fabricated information generated by
language models based on learned patterns rather than facts.
, 4|Page
......ANSWER......What are AI hallucinations and why do they pose a
challenge to reliability?
AI-generated misinformation is unintentional false information
produced by AI, while disinformation is deliberately deceptive.
......ANSWER......How does AI-generated misinformation differ from
disinformation?
Inappropriate and offensive content from AI results from biased training
data containing harmful or prejudiced material. ......ANSWER......What
primarily causes AI models to generate inappropriate or offensive
content?
Robust content filtering, ethical guidelines, transparency, human
oversight, and user education mitigate inappropriate AI content.
......ANSWER......Which strategies help reduce the risk of AI generating
harmful or offensive content?
Data accuracy is the correctness of data within datasets, while data
integrity ensures consistency and reliability throughout its lifecycle.
......ANSWER......What is the difference between data accuracy and data
integrity in AI systems?
DALL-E generates images based on detailed textual descriptions.
......ANSWER......What is a primary function of DALL-E?
WGU D685 - PRACTICAL APPLICATIONS OF PROMPT OA
AND PA | FREQUENTLY TESTED QUESTIONS WITH
CORRECT ANSWERS | BRAND NEW!
Consistency in AI responses builds user trust and encourages continued
use of AI services. ......ANSWER......Why is consistency important in AI-
generated responses?
Effective prompts are clear, structured, and detailed, including modifiers
and iterative refinement for improved AI output. ......ANSWER......What
characteristics define successful AI prompts?
Scenario-based prompts with specificity and clarity produce more
relevant and engaging AI-generated text. ......ANSWER......Why is
specificity important in prompts for AI text generation scenarios?
Few-shot learning uses examples to guide AI, while zero-shot relies on
pre-existing knowledge without examples. ......ANSWER......How do few-
shot and zero-shot prompting techniques differ in guiding AI responses?
Tree-of-thought prompting helps AI explore multiple solutions before
selecting the best one. ......ANSWER......Which prompting technique
encourages AI to evaluate multiple reasoning paths before responding?
,2|Page
Sampling bias occurs when AI training data does not represent the
entire population accurately, leading to skewed outcomes.
......ANSWER......What is sampling bias in AI?
Measurement bias arises from errors or inaccuracies in data collection,
such as leading survey questions. ......ANSWER......Which scenario
exemplifies measurement bias?
Data bias can create feedback loops where AI responses reinforce
existing user viewpoints, limiting perspective diversity.
......ANSWER......How can repeated prompting lead to bias
reinforcement in AI systems?
User confirmation bias leads AI to reinforce users' existing beliefs,
limiting exposure to diverse perspectives. ......ANSWER......What is user
confirmation bias in AI interactions?
Algorithmic bias arises from assumptions in AI design that can amplify
existing data biases. ......ANSWER......What is algorithmic bias and how
does it affect AI outcomes?
Mitigating bias involves diverse data, bias detection tools, transparency,
and continuous monitoring. ......ANSWER......What strategies help
mitigate data bias in AI systems?
,3|Page
Bias and fairness, transparency and accountability, privacy, autonomy
and control, employment impact, and moral decision-making.
......ANSWER......What are the key ethical areas to consider when
evaluating AI systems?
Explainable AI (XAI) provides understandable justifications for AI
decisions and supports accountability. ......ANSWER......Why is
transparency (or explainability) important in AI systems?
Autonomy in AI (e.g., self-driving cars) raises questions about human
oversight and who is responsible for decisions made by autonomous
systems. ......ANSWER......What ethical issues arise from deploying
autonomous AI systems?
Extensive data collection, insecure storage, poor anonymization, and
inference of sensitive traits can all threaten individual privacy.
......ANSWER......What are the main privacy risks associated with AI data
practices?
Federated learning, strong encryption, privacy-by-design, data
minimization, and regular audits help protect user data and privacy.
......ANSWER......Which practical strategies reduce privacy risks when
developing and deploying AI systems?
AI hallucinations are false or fabricated information generated by
language models based on learned patterns rather than facts.
, 4|Page
......ANSWER......What are AI hallucinations and why do they pose a
challenge to reliability?
AI-generated misinformation is unintentional false information
produced by AI, while disinformation is deliberately deceptive.
......ANSWER......How does AI-generated misinformation differ from
disinformation?
Inappropriate and offensive content from AI results from biased training
data containing harmful or prejudiced material. ......ANSWER......What
primarily causes AI models to generate inappropriate or offensive
content?
Robust content filtering, ethical guidelines, transparency, human
oversight, and user education mitigate inappropriate AI content.
......ANSWER......Which strategies help reduce the risk of AI generating
harmful or offensive content?
Data accuracy is the correctness of data within datasets, while data
integrity ensures consistency and reliability throughout its lifecycle.
......ANSWER......What is the difference between data accuracy and data
integrity in AI systems?
DALL-E generates images based on detailed textual descriptions.
......ANSWER......What is a primary function of DALL-E?
