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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q318-Q323):

NEW QUESTION # 318
A project manager overseeing an AI project is concerned about potential data privacy violations.
Before progressing further with the project, what should the project manager do to address this concern?

A. Conduct a thorough compliance audit against data regulations.
B. Implement real-time monitoring tools for data access control.
C. Develop a comprehensive data risk management framework.
D. Establish a cross-functional team to handle data governance.

Answer: A

Explanation:
Conducting a thorough compliance audit against applicable data regulations is the appropriate step before progressing because it verifies whether the project's data collection, processing, storage, access, and usage practices meet privacy and regulatory requirements.

NEW QUESTION # 319
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A. Nothing - running automation projects like autonomous projects is the correct thing to do.
B. Applying a hybrid approach of automation and AI best practices would have achieved better results.
C. Senior management should become involved in the project.
D. Everyone on the team should understand the differences between automation and autonomous systems.

Answer: D

NEW QUESTION # 320
Eight months after deployment, a credit risk model's performance has gradually degraded. The input data pipeline is functioning correctly, but economic conditions have changed significantly since training.
What is the most likely cause?

A. Concept drift in the relationship between features and outcomes
B. Data leakage in the original training set
C. Insufficient model complexity
D. Infrastructure latency in the serving environment

Answer: A

Explanation:
Concept drift occurs when the statistical relationship between input features and the target variable changes over time. Because the model encodes historical relationships, shifting economic conditions invalidate its learned patterns and require retraining on current data.

NEW QUESTION # 321
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?

A. Gradient Descent
B. Multivariate regression
C. Dimensionality Reduction
D. Data selection

Answer: C

Explanation:
Dimensionality reduction reduces the number of input variables, simplifying the dataset while preserving important information for modeling.

NEW QUESTION # 322
An inexperienced team is training a neural network model on a desktop computer which is taking a significant amount of time. Which recommended approach will increase the speed of the model training time?

A. Utilize a contractor to provide the model training portion.
B. Train the model over multiple desktop computers.
C. Divide the dataset into multiple smaller datasets and train the model on each dataset over a desktop computer.
D. Train the model on graphics processing units (GPUs).

Answer: D

Explanation:
Graphics processing units are designed for highly parallel computations, which makes them well suited for neural network training. Using GPUs can significantly reduce training time compared with relying on a standard desktop CPU.

NEW QUESTION # 323
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