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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q79-Q84):

NEW QUESTION # 79
In the early stages of an AI project, the team needs to determine the types of environments and devices where the AI solution will be used. This information is crucial to ensure a successful implementation. Which action should the project manager implement first?

A. Conduct comprehensive user experience research.
B. Hold workshops with end users to gather feedback.
C. Draft a detailed usage scenario analysis.
D. Perform a technical requirements audit.

Answer: C

Explanation:
Drafting a detailed usage scenario analysis identifies where, how, by whom, and on which devices the AI solution will be used. This provides the practical context needed to define environments, user interactions, deployment needs, and implementation constraints early in the project.

NEW QUESTION # 80
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. Senior management should become involved in the project.
C. Everyone on the team should understand the differences between automation and autonomous systems.
D. Applying a hybrid approach of automation and AI best practices would have achieved better results.

Answer: C

NEW QUESTION # 81
An energy technology company needs to convey data understanding to the leadership team for an AI project aimed at predicting equipment failures. The project involves analyzing sensor data from various equipment. Which necessary initial task should the project manager take?

A. Construct a graphical model using DALL-E to depict failure predictions.
B. Develop a preprocessing pipeline to clean and normalize sensor data.
C. Employ a support vector machine to classify sensor data.
D. Use a generative adversarial network (GAN) to simulate equipment behavior.

Answer: B

Explanation:
Developing a preprocessing pipeline is the necessary initial task because sensor data must be cleaned, normalized, and prepared before it can be reliably analyzed, understood, or used to predict equipment failures.

NEW QUESTION # 82
A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?

A. Testing against validation datasets
B. Running multiple end-user acceptance tests
C. Conducting a series of penetration tests
D. Implementing an impact evaluation

Answer: A

Explanation:
Testing against validation datasets confirms whether the model meets the selected performance thresholds before release. This provides objective evidence that the model performs reliably on data separate from training data and is ready for operationalization.

NEW QUESTION # 83
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?

A. Research what Third-Party Models are available and purchase them to keep the project moving
B. Ask your customer service team to generate additional data for you to use for the project
C. Do not move forward with the project
D. Use the data that you have and keep the project moving

Answer: A

NEW QUESTION # 84
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