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| Section | Weight | Objectives |
|---|---|---|
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Model Development and Iteration | - Model building and validation
| |
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 91
A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation. What is represented in this situation?
Answer: B
Explanation:
Transparency is represented because the team is documenting AI decisions, rationale, impacts, and implementation issues so future reviewers can understand how and why choices were made.
This supports auditability, accountability, and traceability across the AI project.
NEW QUESTION # 92
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?
Answer: A
Explanation:
Insufficient context prevents the model from understanding the intended goal, constraints, audience, or required details. As a result, the generated responses are likely to be vague, misaligned, or not relevant to the specific task.
NEW QUESTION # 93
In preparation for an AI solution transition, the project team needs to ensure compliance with data regulations and ethical standards. They must identify and address potential privacy and transparency issues before moving forward.
What is the outcome if these requirements are neglected?
Answer: B
Explanation:
Neglecting privacy, transparency, and regulatory requirements exposes the organization to noncompliance. This can result in legal action, fines, sanctions, and other regulatory penalties during or after the AI solution transition.
NEW QUESTION # 94
A project manager is considering the feasibility of an AI solution for a project aimed at improving customer service response times. They need to decide whether the project requirements can be solved with existing technologies or whether AI is the best approach. Which principle do the project manager's activities represent?
Answer: C
Explanation:
Evaluating cognitive alternatives means determining whether the project requirements truly need AI-based cognitive capabilities or can be addressed using existing technologies, traditional automation, or non-AI approaches.
NEW QUESTION # 95
A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address risks related to data accuracy, privacy, and misuse. What represents the highest risk to the agency?
Answer: A
Explanation:
Within PMI-CPMAI's "Support Responsible and Trustworthy AI Efforts," privacy and security are treated as core, high-severity risks because they can trigger regulatory violations, reputational damage, and harm to individuals. PMI explicitly calls out the need to establish a privacy/security plan with encryption and access controls, privacy impact assessments, and secure handling of personally identifiable information (PII) across the AI lifecycle. If user data is stored in an unsecured database, the agency faces immediate exposure to breach, unauthorized access, and misuse--risks that are typically higher impact than stale data, vendor reliance, or even lack of transparency. In PMI guidance on AI data life cycle management, prolonged retention and weak security increase breach likelihood over time, making insecure storage a critical vulnerability that undermines trust and compliance. While transparency gaps are serious (PMI also emphasizes explainability requirements and audit trails), a direct security failure that exposes user data is generally the most acute and consequential risk because it can cause harm quickly and irreversibly, and it can halt the program through legal and policy intervention.
NEW QUESTION # 96
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