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PMI CPMAI Exam Syllabus Topics:

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

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Free PDF Quiz 2026 PMI CPMAI: First-grade Valid Cognitive Project Management in AI (PMI-CPMAI) Mock Test

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q31-Q36):

NEW QUESTION # 31
In an aerospace project focused on predictive maintenance using AI, the project team is facing challenges in coordinating the AI models' operationalization across various manufacturing sites.
Strong governance and corporate guardrails are established, but each site has different computational capabilities and network latencies. What is an effective method that helps to ensure consistent AI performance across these sites?

A. Operationalizing a decentralized AI architecture
B. Implementing a centralized AI model repository
C. Utilizing cloud-based AI services uniformly
D. Using site-specific AI model tuning

Answer: D

Explanation:
Site-specific AI model tuning helps adapt the operationalized model to each manufacturing site's infrastructure constraints, including computational capacity and network latency, while still working within the established governance and corporate guardrails.

NEW QUESTION # 32
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

A. Make use of this data by having employees pose in the positions required
B. Make use of Synthetic Training Data
C. Make use of this data from surveillance footage
D. Make use of data from different departments

Answer: B

NEW QUESTION # 33
Which of the following best describes the technical definition of Machine Learning?

A. The use of computing technology to enable machines to gain cognitive intelligence.
B. The application of pre-defined rules and algorithms to solve complex problems.
C. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
D. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

Answer: D

Explanation:
This is the classic technical definition of machine learning, emphasizing improvement in task performance through experience measured by a performance metric.

NEW QUESTION # 34
Business stakeholders reviewing a deployed classification model ask why a specific customer record received its prediction. The model is a gradient boosted ensemble.
Which approach best addresses this request?

A. Apply a post-hoc explanation technique such as SHAP or LIME
B. Provide the model's overall accuracy and confusion matrix
C. Publish the feature list used during training
D. Replace the model with logistic regression

Answer: A

Explanation:
Post-hoc explanation techniques attribute an individual prediction to the contribution of each input feature, addressing instance-level questions without replacing the model. Aggregate performance metrics describe overall behavior but cannot explain a single decision.

NEW QUESTION # 35
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 see what pre-trained models your company already owns and use those.
B. Evaluate your data and use only what you have and build all models in house.
C. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
D. 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.

Answer: D

NEW QUESTION # 36
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