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| Section | Objectives |
|---|---|
| AI Risk Management | - Risk Frameworks and Standards
|
| AI Laws, Regulations, and Standards | - Regional and National Regulations
|
| AI Governance Implementation | - Data Governance for AI
|
| Foundations of AI Governance | - AI Governance Fundamentals
|
| AI Governance Management | - Life Cycle Management
|
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NEW QUESTION # 130
CASE STUDY
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.
To 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 deploy 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 AI tool to help make hiring decisions EXCEPT?
Answer: C
Explanation:
The correct answer is B. "Candidate quality" is not a negative consequence of using AI-rather, it is the intended benefit of using such tools (e.g., more efficient filtering of strong candidates).
From the AIGP ILT Guide:
"Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring.
These risks may arise when AI models replicate biases present in training data or obscure the decision logic." AI Governance in Practice Report 2024 (Bias and Fairness Section) also warns:
"Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups." Candidate quality is a goal, not a risk, making B the correct answer for what is not a negative outcome.
NEW QUESTION # 131
CASE STUDY
Please use the following to answer the next question:
A small local flower delivery company operates a few stores and has a limited IT budget. To reduce the time spent on customer service, which is a major drain on employee time, the company plans to implement a simple but high-performance generative AI chatbot on its website. The chatbot will provide real-time, fact- based responses to customer inquiries and assist with order processing, helping to reduce incoming phone calls.
The company currently uses a single on-premises server for its order database and email system and has not yet partnered with a cloud provider.
An internal data privacy policy governs the use of customer data. This policy, written before the adoption of AI, allows the use or transfer of customer data beyond name and contact information but requires that all such data be deleted after the order is completed. The company prefers to maintain this policy without changes.
What deployment option is the simplest, quickest, and most cost-effective option across the various potential AI models for this particular company?
Answer: A
Explanation:
Software-as-a-Service accessed through an API is the most practical deployment approach for a small organization with limited IT infrastructure and budget. SaaS allows the company to consume a provider- managed application without purchasing and maintaining the substantial computing infrastructure normally needed for modern generative AI. Under NIST ' s cloud-computing model, SaaS consumers use provider applications while the provider manages the underlying network, servers, operating systems, storage, and much of the supporting infrastructure. This makes implementation comparatively rapid and scalable. Running the model on the existing on-premises server could require hardware upgrades and specialized maintenance.
A private-cloud deployment introduces additional infrastructure and administrative complexity. Edge deployment on customers ' devices would also create substantial technical and compatibility challenges.
Therefore, considering the company ' s resources and desired speed of deployment, C is the strongest option.
NEW QUESTION # 132
Which of the following may bepermissible usesof an AI system under the EU AI ActEXCEPT?
Answer: C
Explanation:
The correct answer isD.Emotion recognition in the workplaceis flagged asunacceptable or highly restrictedunder the EU AI Act due to its intrusive nature and potential for misuse.
From the AIGP ILT Guide - EU AI Act Training Module:
"AI systems that monitor individuals' emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5." AI Governance in Practice Report2025supports this interpretation:
"Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted." Other uses listed-such as emergency response or emotion detection in healthcare-may fall underlawful and beneficial uses, especially whenjustified by public interest.
NEW QUESTION # 133
What is the most important reason for requiring collaboration among cross-functional stakeholder teams during the AI development lifecycle?
Answer: B
Explanation:
Collaboration among cross-functional teams ensures clear accountability across different AI development stages, improving governance and risk management.
NEW QUESTION # 134
All of the following are potential benefits of using private over public LLMs EXCEPT:
Answer: C
Explanation:
Using a private LLM does not inherently reduce the possibility of hallucinations; hallucination is a core limitation of LLM architectures regardless of whether they are public or private.
NEW QUESTION # 135
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