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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q244-Q249):

NEW QUESTION # 244
A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A. Enhancing with data augmentation techniques
B. Implementing an AI data needs assessment
C. Applying predictive data algorithms
D. Performing systematic data validation

Answer: D

Explanation:
Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.

NEW QUESTION # 245
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

A. Invest in technology solutions to solve the problem
B. Start with basic automation and introduce AI only as needed
C. Have the right culture for AI
D. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes

Answer: C

NEW QUESTION # 246
A sponsor asks the project manager to commit to a fixed delivery date and a specific model accuracy figure at project initiation.
How should the project manager respond?

A. Commit to both and manage variance through change control
B. Explain that achievable accuracy cannot be known before data and modeling work, and propose phase-gate commitments
C. Decline to provide estimates until the model is trained
D. Commit to the date and treat accuracy as a stretch goal

Answer: B

Explanation:
Achievable model performance depends on data quality and characteristics that are unknown at initiation. Committing to phase-level outcomes with go/no-go decision points gives the sponsor control and visibility without requiring an unsupportable guarantee.

NEW QUESTION # 247
A project manager is leading a complex project for a global financial institution. The project is developing an AI-driven system for real-time fraud detection and risk management. The system needs to adhere to all financial regulations. The project manager has identified skills gaps with the existing available resources. What should the project manager do?

A. Engage consultants to fill the expertise gap.
B. Delay the project until internal expertise is developed.
C. Allocate additional budget for consultant AI training.
D. Proceed with the project until external expertise is needed.

Answer: A

Explanation:
Engaging consultants helps fill immediate expertise gaps in a complex, regulated AI project where specialized knowledge in fraud detection, risk management, AI implementation, and financial compliance is required to proceed effectively.

NEW QUESTION # 248
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

A. Provide a means to have a different team on the project
B. Provide a means to have contestability of the algorithm selected
C. Provide a means to reverse-engineer the algorithm to inspect its performance
D. Provide a means to interpret AI results so that cause and effect can be represented

Answer: D

Explanation:
When algorithms lack inherent explainability, providing interpretable AI results helps users understand cause and effect, improving trust and transparency.

NEW QUESTION # 249
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