WGU D685 ACTUAL QUESTIONS AND
ANSWERS EXAMPREP STUDY GUIDE
◉ customization
Answer: features and functionalities that allow users to adjust and
refine the generated images, such as iterative feedback, parameter
adjustments, and style variations, enhancing creative control and
output relevance
◉ tool creation
Answer: the process of designing and developing new software
applications, artistic content, design prototypes, and other
innovative products using the capabilities of generative AI
◉ speech recognition
Answer: AI technology enables computers to transcribe spoken
words into text, facilitating hands-free operation and natural
language interfaces
◉ voice recognition
Answer: the process of distinguishing and confirming the
speaker's identity, facilitating secure authentication systems and
tailored user interactions
◉ optical character recognition (OCR)
,Answer: the AI-driven technology that converts printed or
handwritten text into machine-readable format, enabling
automated data entry, document processing, and image text
extraction
◉ data
Answer: the raw information, often in large volumes, used to train,
validate, and test machine learning models, enabling them to
learn patterns, make predictions, and perform tasks
◉ data cleaning
Answer: the process of identifying and correcting errors,
inconsistencies, and inaccuracies in datasets to ensure the quality
and reliability of the data used for analysis and machine learning
◉ classification
Answer: the process of categorizing data into predefined classes
or groups based on input features using a machine learning
algorithm
◉ data visualization
Answer: the graphical representation of data and information to
communicate insights and patterns effectively
◉ prediction
, Answer: the process of using algorithms and models to forecast
future outcomes or trends based on historical data and patterns
◉ preprocessing
Answer: the initial step in data analysis where raw data is cleaned,
formatted, and transformed to prepare it for further study, often
involving tasks such as handling missing values, removing
duplicates, and standardizing data formats
◉ fairness in AI
Answer: the equitable treatment of individuals and groups across
different demographic categories, regardless of factors such as
race, gender, or socioeconomic status, ensuring that AI systems do
not produce or perpetuate unfair or discriminatory outcomes
◉ transparency in AI
Answer: the practice of making AI systems and their decision-
making processes understandable and explainable to users and
stakeholders, enhancing trust, accountability, and reliability
◉ bias in AI
Answer: systematic errors or inaccuracies in AI algorithms that
result in unfair or discriminatory outcomes, often stemming from
biased training data, algorithmic design, or implementation
◉ Data is the fuel that drives AI systems, enabling pattern
recognition, predictions, and human-like responses.
ANSWERS EXAMPREP STUDY GUIDE
◉ customization
Answer: features and functionalities that allow users to adjust and
refine the generated images, such as iterative feedback, parameter
adjustments, and style variations, enhancing creative control and
output relevance
◉ tool creation
Answer: the process of designing and developing new software
applications, artistic content, design prototypes, and other
innovative products using the capabilities of generative AI
◉ speech recognition
Answer: AI technology enables computers to transcribe spoken
words into text, facilitating hands-free operation and natural
language interfaces
◉ voice recognition
Answer: the process of distinguishing and confirming the
speaker's identity, facilitating secure authentication systems and
tailored user interactions
◉ optical character recognition (OCR)
,Answer: the AI-driven technology that converts printed or
handwritten text into machine-readable format, enabling
automated data entry, document processing, and image text
extraction
◉ data
Answer: the raw information, often in large volumes, used to train,
validate, and test machine learning models, enabling them to
learn patterns, make predictions, and perform tasks
◉ data cleaning
Answer: the process of identifying and correcting errors,
inconsistencies, and inaccuracies in datasets to ensure the quality
and reliability of the data used for analysis and machine learning
◉ classification
Answer: the process of categorizing data into predefined classes
or groups based on input features using a machine learning
algorithm
◉ data visualization
Answer: the graphical representation of data and information to
communicate insights and patterns effectively
◉ prediction
, Answer: the process of using algorithms and models to forecast
future outcomes or trends based on historical data and patterns
◉ preprocessing
Answer: the initial step in data analysis where raw data is cleaned,
formatted, and transformed to prepare it for further study, often
involving tasks such as handling missing values, removing
duplicates, and standardizing data formats
◉ fairness in AI
Answer: the equitable treatment of individuals and groups across
different demographic categories, regardless of factors such as
race, gender, or socioeconomic status, ensuring that AI systems do
not produce or perpetuate unfair or discriminatory outcomes
◉ transparency in AI
Answer: the practice of making AI systems and their decision-
making processes understandable and explainable to users and
stakeholders, enhancing trust, accountability, and reliability
◉ bias in AI
Answer: systematic errors or inaccuracies in AI algorithms that
result in unfair or discriminatory outcomes, often stemming from
biased training data, algorithmic design, or implementation
◉ Data is the fuel that drives AI systems, enabling pattern
recognition, predictions, and human-like responses.
