2025 AIGP–100% FREE PASS4SURE EXAM PREP | PROFESSIONAL AIGP PRACTICE TEST PDF

2025 AIGP–100% Free Pass4sure Exam Prep | Professional AIGP Practice Test Pdf

2025 AIGP–100% Free Pass4sure Exam Prep | Professional AIGP Practice Test Pdf

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Tags: Pass4sure AIGP Exam Prep, AIGP Practice Test Pdf, AIGP Exam Course, Exam AIGP Quizzes, AIGP Valid Test Pdf

With these mock exams, it is easy to track your progress by monitoring your marks each time you go through the AIGP practice test. Our AIGP practice exams will give you an experience of attempting the AIGP original examination. You will be able to deal with the actual exam pressure better when you have already experienced it in our IAPP AIGP practice exams.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 3
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 4
  • Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 5
  • 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 6
  • 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.

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AIGP Practice Test Pdf - AIGP Exam Course

The PassExamDumps IAPP Certified Artificial Intelligence Governance Professional (AIGP) PDF format of questions is user-friendly, portable, and printable that's easy to use on smartphones, laptops, and tablets. This way, you can prepare for the AIGP test anywhere without time restrictions. For those who prefer a traditional reading experience, PassExamDumps IAPP Certified Artificial Intelligence Governance Professional (AIGP) PDF questions also provides the option to print the AIGP questions, and read it in a convenient paper format. This flexibility empowers AIGP candidates to study anywhere and anytime, adapting to their individual preferences and schedules.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q117-Q122):

NEW QUESTION # 117
Random forest algorithms are in what type of machine learning model?

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

Answer: A

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 # 118
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. Operational risk.
  • B. Reputational risk.
  • C. Privacy risk.
  • D. Security risk.

Answer: C

Explanation:
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is privacy risk. Combining data sets, especially in healthcare, often involves handling sensitive information that could lead to privacy breaches if not managed properly. De-identified data can still pose re-identification risks when combined with other data sets. Ensuring privacy involves implementing robust data protection measures, maintaining compliance with privacy regulations such as HIPAA, and conducting thorough privacy impact assessments. Reference: AIGP Body of Knowledge on Data Privacy and Security.


NEW QUESTION # 119
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?

  • A. Corrective actions
  • B. Verification of CE marking
  • C. Communication with national authorities
  • D. Registration in EU Database

Answer: D

Explanation:
The correct answer is C. Registration in the EU database is an obligation of providers of high-risk AI systems-not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report 2024:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus on verification and communication, not formal registration.


NEW QUESTION # 120
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. Define a model-validation methodology.
  • B. Perform a readiness assessment.
  • C. Document maintenance teams and processes.
  • D. Identify known edge cases to monitor post-deployment.

Answer: B

Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.


NEW QUESTION # 121
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The marketing company and its tech provider have taken reasonable steps to govern the AI's use, including legal disclosures, impact assessments, and bias mitigation. However, the company wants to take one more step to improve governance and reduce risks related to ongoing oversight and accountability.
While the marketing agency took steps to mitigate its risks, the best additional step would be to:

  • A. Establish a governance committee to oversee the project
  • B. Evaluate the use of AI in the marketing industry to identify best practices
  • C. Negotiate an intellectual property indemnity from the technology company
  • D. Engage a third party to lead the procurement selection process

Answer: A

Explanation:
The correct answer is D. Forming a dedicated governance committee ensures continuous oversight, role clarity, and accountability throughout the AI lifecycle.
From the AIGP ILT Guide - Governance Structures:
"Organizations using AI in high-impact scenarios should establish a governance body responsible for oversight of risk, compliance, and ethical alignment." Also reflected in AI Governance in Practice Report 2024:
"Committees support cross-functional decision-making, provide guidance for updates, and maintain accountability. This is especially critical for high-stakes applications like marketing to diverse audiences." Options A, B, and C are valid supplementary actions, but D offers a long-term and systematic governance mechanism.


NEW QUESTION # 122
......

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