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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing AI	8%	- Managing AI project teams and resources - Stakeholder management - Risk management in AI projects
Topic 2: Data for AI	13%	- DataOps concepts - Data strategy and governance - Data preparation and preprocessing
Topic 3: AI Fundamentals	16%	- AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence
Topic 4: Trustworthy AI	9%	- Privacy and security - Ethical considerations and bias - Transparency and explainability
Topic 5: Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision)
Topic 6: CPMAI Methodology	41%	- Phase VI: Iteration & Monitoring 1. Retraining and model drift management 2. Continuous monitoring and iteration - Phase V: Operationalization 1. Define success criteria and monitoring 2. Plan for deployment and integration - Phase I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions - Phase II: Data Identification & Curation 1. Assess data readiness and quality 2. Identify data needs - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC - Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection

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2026 CPMAI: High Hit-Rate Online Cognitive Project Management in AI (PMI-CPMAI) Lab Simulation

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q218-Q223):

NEW QUESTION # 218
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. Senior management should become involved in the project.
B. Nothing - running automation projects like autonomous projects is the correct thing to do.
C. Applying a hybrid approach of automation and AI best practices would have achieved better results.
D. Everyone on the team should understand the differences between automation and autonomous systems.

Answer: D

NEW QUESTION # 219
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive. To keep your organization competitive, you need to:

A. Make sure all senior leadership is data literate, understands the Vs of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
B. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
C. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
D. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.

Answer: A

Explanation:
Ensuring all senior leadership is data literate and understands big data concepts and their relevance to the organization is critical for strategic decision-making and maintaining a competitive advantage.

NEW QUESTION # 220
A project manager at an IT services company needs to present a business case for adopting an AI-driven cost-benefit analysis tool. The objective is to demonstrate the tool's value compared to existing processes. Which method will meet the project manager's objective?

A. Developing a proof of concept to highlight efficiency gains
B. Using a decision tree to simulate various cost scenarios
C. Implementing a fuzzy logic system for preliminary evaluations
D. Creating a detailed heuristic-based comparison report

Answer: A

Explanation:
Developing a proof of concept demonstrates the AI-driven tool's practical value against existing processes by showing measurable efficiency gains, faster analysis, improved decision support, and potential cost savings before full adoption.

NEW QUESTION # 221
A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains. Which method will help identify the qualified data SMEs?

A. Conducting interviews to assess their knowledge in anomaly detection
B. Evaluating expertise with existing data architectures and their ability to optimize databases
C. Assessing proficiency in developing generative adversarial networks (GANs) and experience in successfully generating synthetic data
D. Examining their expertise in neural network calibration and hyperparameter tuning

Answer: A

Explanation:
Conducting interviews to assess knowledge in anomaly detection helps identify SMEs who understand the specific data patterns, security signals, abnormal behaviors, and domain context needed to support an AI-powered security anomaly detection project.

NEW QUESTION # 222
An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?

A. User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
B. Response time only
C. Number of features delivered
D. Lines of code written

Answer: A

Explanation:
PMI-CPMAI emphasizes establishing acceptable performance metrics and aligning AI outcomes to business value while ensuring responsible and trustworthy practices. For chatbots, business value includes deflection/containment (how many issues are resolved without human agents), customer experience (satisfaction), and operational performance (latency). Risk measures must also be included because trustworthy AI requires governance and compliance controls (privacy/security, transparency, accountability). Therefore, metrics that combine outcomes and controls--user satisfaction, containment, correct escalation/hand-off, and privacy/compliance incident rates--are the most PMI-aligned set.

NEW QUESTION # 223
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