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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Manage AI Model Development and Evaluation	16%	- Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability
Support Responsible and Trustworthy AI Efforts	15%	- Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks
Operationalize AI Solution	17%	- Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes
Identify Business Needs and Solutions	26%	- Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy
Identify Data Needs	26%	- Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q272-Q277):

NEW QUESTION # 272
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?

A. The AI system relies on third-party providers.
B. The AI system is not regularly updated with new data.
C. User data is stored in an unsecured database.
D. The system lacks a transparency process.

Answer: C

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 # 273
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

A. Support Vector Machines (SVM)
B. Neural Networks
C. Naive Bayes
D. Gaussian Mixture

Answer: C

NEW QUESTION # 274
Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Choose all that apply.)

A. Human chain of accountability
B. Algorithm selection
C. Data source identification
D. Continuous System monitoring
E. Vendor procurement methods
F. Key Performance Indicators (KPIs)
G. AI System testing requirements
H. ROI determination

Answer: A,D,F,G

Explanation:
Effective AI governance requires robust system testing, continuous monitoring to detect issues, clear human accountability for decisions, and KPIs to measure system performance and impact.

NEW QUESTION # 275
A financial services organization deploys an AI credit-scoring model. Post-deployment monitoring reveals systematic underprediction for a protected demographic group, potentially affecting loan approvals. Stakeholders are concerned about fairness and compliance risks. What should the project manager prioritize FIRST?

A. Conduct a bias and fairness audit
B. Pause the system indefinitely
C. Retrain the model with additional data
D. Replace the algorithm entirely

Answer: A

Explanation:
A fairness audit identifies root causes before corrective action. Immediate retraining or replacement without diagnosis may worsen bias or introduce compliance issues.

NEW QUESTION # 276
A team is in the early stages of an AI project. They need to ensure they have the necessary data and technology to support AI solution development. What is the first step the project team should complete?

A. Identify the gaps and procure the needed tools.
B. Assess the team's current AI and data expertise.
C. Verify the availability and quality of the required data.
D. Outline the business objectives for the AI project.

Answer: D

Explanation:
Outlining the business objectives should come first because the team must understand what the AI solution is intended to achieve before determining what data, technology, tools, and expertise are required to support development.

NEW QUESTION # 277
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