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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
Topic 2: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
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 System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection
Topic 5: Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Topic 6: AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q107-Q112):

NEW QUESTION # 107
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 the date and treat accuracy as a stretch goal
B. Decline to provide estimates until the model is trained
C. Explain that achievable accuracy cannot be known before data and modeling work, and propose phase-gate commitments
D. Commit to both and manage variance through change control

Answer: C

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 # 108
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

A. Data Governance
B. Data Integration
C. Data Mining or Data Analytics
D. Data preparation

Answer: C

Explanation:
Data mining or data analytics focuses on analyzing datasets to discover patterns, relationships, and insights.

NEW QUESTION # 109
An AI-powered chatbot occasionally generates inappropriate or harmful responses despite strong training performance. The organization wants to reduce risks while maintaining usability. What is the most effective mitigation strategy for this issue?

A. Increase dataset size
B. Implement guardrails and content filters
C. Remove monitoring mechanisms
D. Accelerate deployment cycles

Answer: B

Explanation:
Guardrails control outputs and reduce harmful responses.

NEW QUESTION # 110
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. Model instability
B. Data leakage
C. Bias elimination
D. Underfitting

Answer: A

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

NEW QUESTION # 111
During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
B. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.
C. Evaluate your data and use only what you have and build all models in house.
D. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.

Answer: A

NEW QUESTION # 112
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