AIGP IAPP Exam Questions and Free Practice Test

Question 19

- (Topic 1)
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 they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.

C. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.

D. That the Al can differentiate among ethnic backgrounds of pedestrians.

Correct Answer:A
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.

Question 20

- (Topic 2)
Which type of existing assessment could best be leveraged to create an Al impact assessment?

A. A safety impact assessment.

B. A privacy impact assessment.

C. A security impact assessment.

D. An environmental impact assessment.

Correct Answer:B
A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI. Reference: AIGP Body of Knowledge on Impact Assessments.

Question 21

- (Topic 1)
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

A. The human oversight measures employed.

B. How an individual may contest a decision.

C. The location(s) where data is stored.

D. The fact that an Al system is being used.

Correct Answer:C
Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system's operation and its impact on individuals, rather than on the technical details of data storage locations.

Question 22

- (Topic 2)
Which of the following elements of feature engineering is most important to mitigate the potential bias in an Al system?

A. Feature selection.

B. Feature validation.

C. Feature transformation.

D. Feature importance analysis.

Correct Answer:A
Feature selection is the most important element of feature engineering to mitigate potential bias in an AI system. This process involves choosing the most relevant and representative features from the data set, which directly affects the model’s performance and fairness. By carefully selecting features, data scientists can reduce the influence of biased or irrelevant attributes, ensuring that the AI system is more accurate and equitable. Proper feature selection helps in eliminating biases that might stem from socio-demographic factors or other sensitive variables, leading to a more balanced and fair AI model. Reference: AIGP Body of Knowledge on Fairness in AI and Feature Engineering.

Question 23

- (Topic 1)
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?

A. Data architects.

B. Government regulators.

C. Socio-cultural and technical experts.

D. Legal and privacy governance experts.

Correct Answer:B
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.

Question 24

- (Topic 2)
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.

B. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.

C. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.

D. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Correct Answer:D
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

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