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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q270-Q275):

NEW QUESTION # 270
Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected. What could have been done better to get the team to feel comfortable with this technology and use it? (Choose all that apply.)

A. Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
B. Have upper management relay to employees this tool is to augment, and not replace their jobs.
C. Have the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
D. Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.

Answer: A,B,D

Explanation:
Engaging end users to address their needs, having upper management clearly communicate the augmentation purpose, and providing training all help build trust and adoption of new AI tools among employees.

NEW QUESTION # 271
Enhancing and cleaning data is an important action during which phase of CPMAI?

A. Phase I
B. Phase III
C. Phase V
D. Phase II
E. Phase VI
F. Phase IV

Answer: A

Explanation:
Enhancing and cleaning data is a key activity during Phase I of the CPMAI (Cognitive Project Management for AI) framework, which focuses on data preparation and exploration to ensure quality input for model development.

NEW QUESTION # 272
During the planning phase, a project manager identifies multiple patterns for their AI project.
Which two actions should the project manager do? (Choose two.)

A. Focus on noncognitive parts before addressing cognitive patterns.
B. Select patterns based on stakeholder preference.
C. Combine all patterns into a single comprehensive iteration.
D. Proceed with the most complex pattern to challenge the team.
E. Split the project into iterations for different patterns.

Answer: A,E

Explanation:
Splitting the project into iterations allows each AI pattern to be planned, tested, and refined separately, reducing complexity and improving control. Addressing noncognitive parts first can establish stable process automation and integration foundations before introducing more complex cognitive AI capabilities.

NEW QUESTION # 273
A project team at a healthcare provider is determining whether their patient records are adequate for an AI diagnostic tool. They need to validate that the data covers a broad spectrum of conditions and demographics. What is an effective method to assure data suitability?

A. Analyzing data variance and ensuring balanced sampling
B. Implementing a longitudinal data-gathering approach
C. Performing demographic analysis and stratifying patient data
D. Conducting a cross-sectional study on data diversity

Answer: C

Explanation:
Performing demographic analysis and stratifying patient data helps verify that the records adequately represent different patient groups and a broad range of conditions. This supports data suitability by ensuring the diagnostic AI model is trained and evaluated on representative healthcare data.

NEW QUESTION # 274
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?

A. Assess data completeness.
B. Apply data labeling techniques.
C. Implement data enhancement.
D. Conduct data cleaning.

Answer: A

Explanation:
Assessing data completeness should be done first because the team must verify whether the customer data contains the required fields, records, and coverage before cleaning, enhancing, labeling, or preparing it for the segmentation model.

NEW QUESTION # 275
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