Machine learning is best described as a type of algorithm by which?
B
Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and
improve over time without being explicitly programmed. Option B accurately describes machine
learning by stating that systems can automatically improve from experience through predictive
patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize
patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF
KNOWLEDGE, which covers the basics of AI and machine learning concepts.
You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts
that have a specialty Italian dish made in a traditional fashion without spinach and wine. The
generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not
have the dish.
This information provided by the generative Al tool is an example of what is commonly called?
C
Explanation:
In the context of AI, particularly generative models, "hallucination" refers to the generation of
outputs that are not based on the training data and are factually incorrect or non-existent. The
scenario described involves the generative AI tool providing incorrect and non-existent information
about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE
and various AI literature discussing the limitations and challenges of generative AImodels.
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
B
Explanation:
Typically, actors involved in the AI development life cycle include data architects (who design the
data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally
aware and technically sound), and legal and privacy governance experts (who handle the legal and
privacy aspects). Government regulators, while important, are not directly engaged in the
development process but rather oversee and regulate the industry. Reference: AIGP BODY OF
KNOWLEDGE and AI development frameworks.
A company is working to develop a self-driving car that can independently decide the appropriate
route to take the driver after the driver provides an address.
If they want to make this self-driving car “strong” Al, as opposed to "weak,” the engineers would also
need to ensure?
A
Explanation:
Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to
understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive
abilities. For the self-driving car to be classified as "strong" AI, it would need to possess full human
cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference:
AIGP BODY OF KNOWLEDGE and AI classifications.
Which of the following is NOT a common type of machine learning?
B
Explanation:
The common types of machine learning include supervised learning, unsupervised learning,
reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning;
rather, it is a term often associated with the broader field of cognitive science and psychology.
Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has
decided to utilize artificial intelligence to streamline and improve its customer acquisition and
underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large
language model (“LLM”). In particular, ABC intends to use its historical customer data—including
applications, policies, and claims—and proprietary pricing and risk strategies to provide an initial
qualification assessment of potential customers, which would then be routed t
A
Explanation:
The best approach to enable a customer who wants information on the AI model's parameters for
underwriting purposes is to provide a transparency notice. This notice should explain the nature of
the AI system, how it uses customer data, and the decision-making process it follows. Providing a
transparency notice is crucial for maintaining trust and compliance with regulatory requirements
regarding the transparency and accountability of AI systems.
Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to
ensure that stakeholders, including customers, understand how their data is being used and how
decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are
informed and can make knowledgeable decisions regarding their interactions with AI systems.
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has
decided to utilize artificial intelligence to streamline and improve its customer acquisition and
underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large
language model (“LLM”). In particular, ABC intends to use its historical customer data—including
applications, policies, and claims—and proprietary pricing and risk strategies to provide an initial
qualification assessment of potential customers, which would then be routed a human underwriter
for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness
assessment, and made the decision to deploy the LLM into production. ABC has designated an
internal compliance team to monitor the model during the first month, specifically to evaluate
theaccuracy, fairness, and reliability of its output. After the first month in production, ABC realizes
that the LLM declines a higher percentage of women's loan applications due primarily to women
historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to
deployment?
C
Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their own
judgment to the initial assessment. While AI models can streamline the process, human judgment is
still essential to catch nuances that the model might miss or to account for any biases or errors in the
model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI
systems, particularly in high-stakes areas such as underwriting and loan approvals. Human
underwriters can provide a critical review and ensure that the model's assessments are accurate and
fair, integrating their expertise and understanding of complex cases.
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has
decided to utilize artificial intelligence to streamline and improve its customer acquisition and
underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large
language model (“LLM”). In particular, ABC intends to use its historical customer data—including
applications, policies, and claims—and proprietary pricing and risk strategies to provide an initial
qualification assessment of potential customers, which would then be routed .. human underwriter
for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness
assessment, and made the decision to deploy the LLM into production. ABC has designated an
internal compliance team to monitor the model during the first month, specifically to evaluate the
accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that
the LLM declines a higher percentage of women's loan applications due primarily to women
historically receiving lower salaries than men.
During the first month when ABC monitors the model for bias, it is most important to?
A
Explanation:
During the first month of monitoring the model for bias, it is most important to continue disparity
testing. Disparity testing involves regularly evaluating the model's decisions to identify and address
any biases, ensuring that the model operates fairly across different demographic groups.
Reference: Regular disparity testing is highlighted in the AIGP Body of Knowledge as a critical
practice for maintaining the fairness and reliability of AI models. By continuously monitoring for and
addressing disparities, organizations can ensure their AI systems remain compliant with ethical and
legal standards, and mitigate any unintended biases that may arise in production.
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has
decided to utilize artificial intelligence to streamline and improve its customer acquisition and
underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large
language model (“LLM”). In particular, ABC intends to use its historical customer data—including
applications, policies, and claims—and proprietary pricing and risk strategies to provide an initial
qualification assessment of potential customers, which would then be routed t
B
Explanation:
Providing the loan applicants with information about the model capabilities and limitations would
not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating
the model's decisions for biases and ensuring equitable treatment across different demographic
groups, rather than informing applicants about the model.
Reference: The AIGP Body of Knowledge outlines that fairness testing involves technical assessments
such as validating decision-making consistency across demographics and using tools to understand
decision factors. While transparency to applicants is important for ethical AI use, it does not
contribute directly to the technical process of fairness testing.
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has
decided to utilize artificial intelligence to streamline and improve its customer acquisition and
underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large
language model (“LLM”). In particular, ABC intends to use its historical customer data—including
applications, policies, and claims—and proprietary pricing and risk strategies to provide an
initialqualification assessment of potential customers, which would then be routed a human
underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness
assessment, and made the decision to deploy the LLM into production. ABC has designated an
internal compliance team to monitor the model during the first month, specifically to evaluate the
accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that
the LLM declines a higher percentage of women's loan applications due primarily to women
historically receiving lower salaries than men.
What is the best strategy to mitigate the bias uncovered in the loan applications?
A
Explanation:
Retraining the model with data that reflects demographic parity is the best strategy to mitigate the
bias uncovered in the loan applications. This approach addresses the root cause of the bias by
ensuring that the training data is representative and balanced, leading to more equitable decision-
making by the AI model.
Reference: The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased
training data to develop fair and reliable AI systems. Retraining the model with balanced data helps
correct biases that arise from historical inequalities, ensuring that the AI system makes decisions
based on equitable criteria.
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train
algorithms?
D
Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to
train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs
are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen
data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model
trained on labeled data (e.g., text recognition, detecting spam in emails).
A company developed Al technology that can analyze text, video, images and sound to tag content,
including the names of animals, humans and objects.
What type of Al is this technology classified as?
B
Explanation:
A multi-modal model is an AI system that can process and analyze multiple types of data, such as
text, video, images, and sound. This type of AI integrates different data sources to enhance its
understanding and decision-making capabilities. In the given scenario, the AI technology that tags
content including names of animals, humans, and objects falls under this category. Reference: AIGP
BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models.
All of the following are common optimization techniques in deep learning to determine weights that
represent the strength of the connection between artificial neurons EXCEPT?
C
Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for
artificial neurons. Common techniques include gradient descent, momentum, and backpropagation.
Autoregression is more commonly associated with time-series analysis and forecasting rather than
neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common
optimization techniques used in deep learning.
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running
Al applications?
A
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to
run many tasks concurrently, which significantly enhances processing speed. This parallel processing
capability makes GPUs ideal for handling the large-scale computations required in AI and deep
learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute
infrastructure in AI applications.
Which of the following best defines an "Al model"?
D
Explanation:
An AI model is best defined as a program that has been trained on a set of data to find patterns
within that data. This definition captures the essence of machine learning, where the model learns
from the data to make predictions or decisions. Reference: AIGP BODY OF KNOWLEDGE, which
provides a detailed explanation of AI models and their training processes.