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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.
Topic 2	Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 3	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q32-Q37):

NEW QUESTION # 32
Which of the following best defines an "Al model"?

A. A corpus of data which an Al algorithm analyzes to make predictions.
B. A system that applies defined rules to execute tasks.
C. A system of controls that is used to govern an Al algorithm.
D. A program that has been trained on a set of data to find patterns within the data.

Answer: 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.

NEW QUESTION # 33
In the machine learning context, feature engineering is the process of?

A. Creating learning schema for a model apply.
B. Extracting attributes and variables from raw data.
C. Developing guidelines to train and test a model.
D. Converting raw data into clean data.

Answer: B

Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.

NEW QUESTION # 34
Random forest algorithms are in what type of machine learning model?

A. Symbolic.
B. Generative.
C. Discriminative.
D. Natural language processing.

Answer: C

Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms.
They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks.

NEW QUESTION # 35
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?

A. Adapting the existing governance structure algorithmic decision-making.
B. Determining the level of human involvement in algorithmic decision-making.
C. Establishing communications and collaboration among stakeholders.
D. Employing human-over-the-loop protocols for high-risk systems.

Answer: D

Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.

NEW QUESTION # 36
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?

A. Perform a privacy impact assessment.
B. Segregate the data sets.
C. Combine only anonymized data.
D. Review the terms of use.

Answer: D

Explanation:
Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.

NEW QUESTION # 37
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