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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection
AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q312-Q317):

NEW QUESTION # 312
Your team is looking for a short term ROI project and decides that an AI enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition. What's the best course of action with how to proceed?

A. Do not move forward until you can determine a clear problem definition.
B. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
C. Do not move forward and cancel the project all together.
D. Move forward with the project as planned. The problem definition will become clear later on in the project.

Answer: A

Explanation:
A clear problem definition is essential before moving forward; without it, the project risks misalignment with business goals and wasted resources, so progress should be paused until clarity is achieved.

NEW QUESTION # 313
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn't have access to all the data it needs.
What's the best course of action?

A. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
B. Cautiously move forward knowing you may need to pause mid-project which is ok.
C. Move ahead as planned so you stay on time with your project.
D. Do not move forward until you have access to all the data you need.

Answer: D

NEW QUESTION # 314
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?

A. Implementing a data encryption protocol
B. Using an explainability framework
C. Performing continuous monitoring and auditing
D. Conducting a stakeholder impact analysis

Answer: C

Explanation:
PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one- time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI-CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation--foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes.

NEW QUESTION # 315
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions.
What is the best approach for handling this project?

A. Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
B. Run each pattern in isolation, with separate teams
C. Run it as a hybrid approach and some phases are run separately while other phases are combined together
D. Run it as one project, combining teams, data requirements, and project needs

Answer: C

NEW QUESTION # 316
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?

A. There is no issue. Pipelines can be reused as needed between projects
B. Pipelines are pattern and model need specific.
C. Pipelines are model operationalization need specific.
D. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.

Answer: B

Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.

NEW QUESTION # 317
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