AIGP Exam UPDATED Study ACTUAL
QUESTIONS AND CORRECT ANSWERS
Artificial intelligence - CORRECT ANSWER Artificial intelligence is a broad term
used to describe an engineered system that uses various computational techniques to perform
or automate tasks. This may include techniques, such as machine learning, where machines
learn from experience, adjusting to new input data and potentially performing tasks
previously done by humans. More specifically, it is a field of computer science dedicated to
simulating intelligent behavior in computers. It may include automated decision-making.
→ Acronym: AI
AI governance - CORRECT ANSWER A system of laws, policies, frameworks,
practices and processes at international, national and organizational levels. AI governance
helps various stakeholders implement, manage and oversee the use of AI technology. It also
helps manage associated risks to ensure AI aligns with stakeholders' objectives, is developed
and used responsibly and ethically, and complies with applicable requirements.
Machine learning - CORRECT ANSWER A subfield of AI involving algorithms that
enable computer systems to iteratively learn from and then make decisions, inferences or
predictions based on input data. These algorithms build a model from training data to perform
a specific task on new data without being explicitly programmed to do so.
Machine learning implements various algorithms that learn and improve by experience in a
problem-solving process that includes data cleansing, feature selection, training, testing and
validation. Companies and government agencies deploy machine learning algorithms for
tasks such as fraud detection, recommender systems, customer inquiries, health care, or
transport and logistics.
→ Acronym: ML
Algorithm - CORRECT ANSWER A procedure or set of instructions and rules
designed to perform a specific task or solve a particular problem, using a computer.
Supervised learning - CORRECT ANSWER A subset of machine learning where the
model (see machine learning model) is trained on labeled input data with known desired
outputs. These two groups of data are sometimes called predictors and targets, or independent
and dependent variables, respectively. This type of learning is useful for classification or
, regression. The former refers to training an AI to group data into specific categories and the
latter refers to making predictions by understanding the relationship between two variables.
Classification model - CORRECT ANSWER A type of model (see machine learning
model) used in machine learning that is designed to take input data and sort it into different
categories or classes.
→ Sometimes referred to as classifiers.
Unsupervised learning - CORRECT ANSWER A subset of machine learning where the
model is trained by looking for patterns in an unclassified dataset with minimal human
supervision. The AI is provided with preexisting unlabeled datasets and then analyzes those
datasets for patterns. This type of learning is useful for training an AI for techniques such as
clustering data (outlier detection, etc.) and dimensionality reduction (feature learning,
principal component analysis, etc.).
Clustering - CORRECT ANSWER An unsupervised machine learning method where
patterns in the data are identified and evaluated, and data points are grouped accordingly into
clusters based on their similarity.
→ Sometimes referred to as clustering algorithms.
Reinforcement learning - CORRECT ANSWER A machine learning method that trains
a model to optimize its actions within a given environment to achieve a specific goal, guided
by feedback mechanisms of rewards and penalties. This training is often conducted through
trial-and-error interactions or simulated experiences that do not require external data. For
example, an algorithm can be trained
to earn a high score in a video game by having its efforts evaluated and rated according to
success toward the goal.
Semi-supervised learning - CORRECT ANSWER A subset of machine learning that
combines both supervised and unsupervised learning by training the model on a large amount
of unlabeled data and a small amount of labeled data. This avoids the challenges of finding
large amounts of labeled data for training the model. Generative AI commonly relies on semi-
supervised learning.
QUESTIONS AND CORRECT ANSWERS
Artificial intelligence - CORRECT ANSWER Artificial intelligence is a broad term
used to describe an engineered system that uses various computational techniques to perform
or automate tasks. This may include techniques, such as machine learning, where machines
learn from experience, adjusting to new input data and potentially performing tasks
previously done by humans. More specifically, it is a field of computer science dedicated to
simulating intelligent behavior in computers. It may include automated decision-making.
→ Acronym: AI
AI governance - CORRECT ANSWER A system of laws, policies, frameworks,
practices and processes at international, national and organizational levels. AI governance
helps various stakeholders implement, manage and oversee the use of AI technology. It also
helps manage associated risks to ensure AI aligns with stakeholders' objectives, is developed
and used responsibly and ethically, and complies with applicable requirements.
Machine learning - CORRECT ANSWER A subfield of AI involving algorithms that
enable computer systems to iteratively learn from and then make decisions, inferences or
predictions based on input data. These algorithms build a model from training data to perform
a specific task on new data without being explicitly programmed to do so.
Machine learning implements various algorithms that learn and improve by experience in a
problem-solving process that includes data cleansing, feature selection, training, testing and
validation. Companies and government agencies deploy machine learning algorithms for
tasks such as fraud detection, recommender systems, customer inquiries, health care, or
transport and logistics.
→ Acronym: ML
Algorithm - CORRECT ANSWER A procedure or set of instructions and rules
designed to perform a specific task or solve a particular problem, using a computer.
Supervised learning - CORRECT ANSWER A subset of machine learning where the
model (see machine learning model) is trained on labeled input data with known desired
outputs. These two groups of data are sometimes called predictors and targets, or independent
and dependent variables, respectively. This type of learning is useful for classification or
, regression. The former refers to training an AI to group data into specific categories and the
latter refers to making predictions by understanding the relationship between two variables.
Classification model - CORRECT ANSWER A type of model (see machine learning
model) used in machine learning that is designed to take input data and sort it into different
categories or classes.
→ Sometimes referred to as classifiers.
Unsupervised learning - CORRECT ANSWER A subset of machine learning where the
model is trained by looking for patterns in an unclassified dataset with minimal human
supervision. The AI is provided with preexisting unlabeled datasets and then analyzes those
datasets for patterns. This type of learning is useful for training an AI for techniques such as
clustering data (outlier detection, etc.) and dimensionality reduction (feature learning,
principal component analysis, etc.).
Clustering - CORRECT ANSWER An unsupervised machine learning method where
patterns in the data are identified and evaluated, and data points are grouped accordingly into
clusters based on their similarity.
→ Sometimes referred to as clustering algorithms.
Reinforcement learning - CORRECT ANSWER A machine learning method that trains
a model to optimize its actions within a given environment to achieve a specific goal, guided
by feedback mechanisms of rewards and penalties. This training is often conducted through
trial-and-error interactions or simulated experiences that do not require external data. For
example, an algorithm can be trained
to earn a high score in a video game by having its efforts evaluated and rated according to
success toward the goal.
Semi-supervised learning - CORRECT ANSWER A subset of machine learning that
combines both supervised and unsupervised learning by training the model on a large amount
of unlabeled data and a small amount of labeled data. This avoids the challenges of finding
large amounts of labeled data for training the model. Generative AI commonly relies on semi-
supervised learning.
