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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
Topic 2: Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
Topic 3: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Define AI business objectives 2. Feasibility and ROI analysis
Topic 4: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Topic 5: AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI
Topic 6: 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 (Q212-Q217):

NEW QUESTION # 212
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?

A. Make sure the model lives on the edge device so it can be used regardless of internet connection
B. Make sure that you can use Generative AI solutions on an edge device
C. Make sure the model lives in a hybrid environment
D. Make sure the model is available over a cloud-based API

Answer: A

Explanation:
For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.

NEW QUESTION # 213
An AI model is frequently retrained with new data to improve performance. However, stakeholders report inconsistent predictions across releases, causing confusion and reduced trust in the system. What risk is most likely occurring in this situation?

A. Data leakage
B. Bias elimination
C. Model instability
D. Underfitting

Answer: C

Explanation:
Frequent retraining without controls leads to inconsistent outputs and instability.

NEW QUESTION # 214
Midway through an iteration, business stakeholders request that the model also predict two additional outcomes not included in the original scope.
What should the project manager do?

A. Reject the request as outside the approved scope
B. Incorporate the request to maintain stakeholder engagement
C. Evaluate the request against the iteration objective and defer it to a subsequent iteration if it does not fit
D. Extend the current iteration timeline to accommodate the additional outcomes

Answer: C

Explanation:
Iterative delivery depends on protecting the current iteration's defined objective while capturing new requirements for future planning. Deferring the request preserves delivery cadence without discarding potentially valuable stakeholder input.

NEW QUESTION # 215
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?

A. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
B. When in doubt, go with the patterns and anomalies pattern as all AI projects are about pattern matching.
C. When in doubt, don't apply any pattern of AI.
D. Apply every pattern to the project.

Answer: A

NEW QUESTION # 216
A company is using an AI model to screen job applications. Over time, the project team notices that the model disproportionately rejects applications from certain educational institutions. What is the first step the project manager should take?

A. Incorporate a fairness constraint into the model's objective function.
B. Adjust the model's decision threshold for applications from these institutions.
C. Analyze the historical data for inherent biases against these institutions.
D. Retrain the model with equal representation from all institutions.

Answer: C

Explanation:
Analyzing the historical data for inherent biases is the first step because the team must identify whether past hiring patterns, labels, or institutional representation caused the model to learn unfair rejection behavior. Understanding the source of bias is necessary before retraining, changing thresholds, or applying fairness controls.

NEW QUESTION # 217
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