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IAPP AIGP Exam Dumps

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Description

Exam Name: Artificial Intelligence Governance Professional
Exam Code: AIGP
Related Certification(s): IAPP Certification Programs Certification
Certification Provider: IAPP
Actual Exam Duration: 160 Minutes
Number of AIGP practice questions in our database: 100 

Expected AIGP Exam Topics, as suggested by IAPP :

  • Module 1: 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.
  • Module 2: Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
  • Module 3: Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
  • Module 4: 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.
  • Module 5: Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
  • Module 6: Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
  • Module 7: Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.

Description

Exam Name: Artificial Intelligence Governance Professional
Exam Code: AIGP
Related Certification(s): IAPP Certification Programs Certification
Certification Provider: IAPP
Actual Exam Duration: 160 Minutes
Number of AIGP practice questions in our database: 100 

Expected AIGP Exam Topics, as suggested by IAPP :

  • Module 1: 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.
  • Module 2: Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
  • Module 3: Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
  • Module 4: 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.
  • Module 5: Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
  • Module 6: Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
  • Module 7: Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.

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Q1. What is the technique to remove the effects of improperly used data from an ML system?

A.Data cleansing.

B. Model inversion.

C. Data de-duplication.

D. Model disgorgement.

Correct Answer: D

Q2. After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A.Perform a readiness assessment.

B. Define a model-validation methodology.

C. Document maintenance teams and processes.

D. Identify known edge cases to monitor post-deployment.

Correct Answer: A

Q3. 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

Q4. 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. The most significant risk from combining the healthcare network's existing data with the clinical research partner data is?

A.Privacy risk.

B. Security risk.

C. Operational risk.

D. Reputational risk.

Correct Answer: A

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