AIGP Valid Exam Experience - AIGP Certification Exam Dumps

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 2	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 3	Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 4	Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
Topic 5	Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 6	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and copyright’s Bill C-27.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
What is the term for an algorithm that focuses on making the best choice achieve an immediate objective at a particular step or decision point, based on the available information and without regard for the longer-term best solutions?

A. Single-lane.
B. Greedy.
C. Optimized.
D. Efficient.

Answer: B

Explanation:
A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.

NEW QUESTION # 35
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. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
B. That the Al can differentiate among ethnic backgrounds of pedestrians.
C. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.
D. That they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.

Answer: 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.

NEW QUESTION # 36
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?

A. Momentum, which improves the convergence speed and stability of neural network training.
B. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
C. Autoregression, which analyzes and makes predictions about time-series data.
D. Backpropagation, which starts from the last layer working backwards.

Answer: 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.

NEW QUESTION # 37
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?

A. Determining the level of human involvement in algorithmic decision-making.
B. Establishing communications and collaboration among stakeholders.
C. Adapting the existing governance structure algorithmic decision-making.
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 # 38
All of the following are reasons to deploy a challenger Al model in addition a champion Al model EXCEPT to?

A. Provide a framework to consider alternatives to the champion model.
B. Retrain the champion model.
C. Automate real-time monitoring of the champion model.
D. Perform testing on the champion model.

Answer: B

Explanation:
Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.
Reference: AIGP BODY OF KNOWLEDGE, sections on model evaluation and management.

NEW QUESTION # 39
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