Three Formats for IAPP AIGP Exam Questions

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In addition to the IAPP AIGP PDF dumps, we also offer IAPP AIGP practice exam software. You will find the same ambiance and atmosphere when you attempt the real IAPP AIGP exam. It will make you practice nicely and productively as you will experience better handling of the IAPP AIGP Questions when you take the actual AIGP exam to grab the IAPP Certified Artificial Intelligence Governance Professional certification.

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	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 4	Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.

>> Latest AIGP Practice Questions <<

Pass4sure AIGP Study Materials | AIGP Test Fee

Exams like the IAPP AIGP exam provided by IAPP are crucial for the advancement of your career. Candidates want to succeed on their IAPP Certified Artificial Intelligence Governance Professional exam. For candidates to study for and successfully pass their chosen certification exam the first time, TestValid provides IAPP Certified Artificial Intelligence Governance Professional AIGP Exam Questions. You may use the top AIGP study resources from TestValid to prepare for the IAPP Certified Artificial Intelligence Governance Professional exam. IAPP AIGP exam questions are a dependable and trustworthy source of training.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
Testing data is defined as a subset of data that is used to?

A. Assess a model's on-going performance in production.
B. Evaluate a model's handling of randomized edge cases.
C. Provide a robust evaluation of a final model.
D. Enable a model to discover and learn patterns.

Answer: C

Explanation:
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.

NEW QUESTION # 53
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent.
The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
All of the following are potential negative consequences created by using the Al tool when making hiring decisions EXCEPT?

A. Reputational harm.
B. Civil rights violations.
C. Intellectual property infringement.
D. Discriminatory treatment.

Answer: C

Explanation:
The potential negative consequences of using an AI tool in hiring include reputational harm (A), civil rights violations (B), and discriminatory treatment (C). These issues stem from biases in the AI system or its misuse, which can lead to unfair hiring practices and legal liabilities. Intellectual property infringement (D) is not a typical consequence of using AI in hiring, as it relates to the unauthorized use of protected intellectual property, which is not directly relevant to the hiring process or the potential biases within AI tools.

NEW QUESTION # 54
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

A. Adjust the model's hyper parameters specific use case.
B. To minimize degradation in prediction accuracy due tochanges in data.
C. Account for new interpretations of the same data.
D. To ensure interpretability of the model's predictions.

Answer: D

Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

NEW QUESTION # 55
A US company has developed an Al system, CrimeBuster 9619, that collects information about incarcerated individuals to help parole boards predict whether someone is likely to commit another crime if released from prison.
When considering expanding to the EU market, this type of technology would?

A. Be subject approval by the relevant EU authority.
B. Be banned under the EU Al Act.
C. Require a detailed conformity assessment.
D. Require the company to register the tool with the EU database.

Answer: C

Explanation:
Under the EU AI Act, high-risk AI systems like CrimeBuster 9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements.

NEW QUESTION # 56
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Tested and Effective.
B. Accountable and Transparent.
C. Explainable and Interpretable.
D. Secure and Resilient.

Answer: C

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.

NEW QUESTION # 57
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THREE FORMATS FOR IAPP AIGP EXAM QUESTIONS

Three Formats for IAPP AIGP Exam Questions