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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q40-Q45):

NEW QUESTION # 40
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team.
What's your best course of action?

A. Find a citizen data scientist to help
B. Outsource the project ASAP
C. Put the project on hold until the resources needed become available
D. Use an AutoML solution

Answer: D

NEW QUESTION # 41
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?

A. Batch prediction
B. Real-time prediction
C. Web service / Microservice
D. Stream learning

Answer: A

Explanation:
Batch prediction suits scenarios needing periodic analysis, like weekly monitoring, by processing accumulated data in scheduled batches.

NEW QUESTION # 42
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

A. As a programmatic approach for automation
B. Used for Hyperpersonalization
C. As an Augmented Intelligence system
D. As an autonomous system removing the human from the loop

Answer: C

NEW QUESTION # 43
An aerospace company is operationalizing an AI system to optimize flight routes. The system must be safe and comply with aviation regulations. The project team is evaluating methods to prevent potential safety risks.
Which method should the project manager use to prevent safety risk issues?

A. Collaborating with regulatory agencies for advice
B. Initiating reviews of black-box flight data for aircraft issues
C. Implementing monitoring for compliance with aviation standards
D. Conducting extensive simulation testing under various scenarios

Answer: D

Explanation:
Extensive simulation testing exposes the AI system to a wide range of normal, abnormal, and edge-case flight conditions before operational use. This helps identify unsafe behaviors and failure modes early, allowing the team to address safety risks before they affect real-world flight operations.

NEW QUESTION # 44
You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with. Which algorithm is the best choice given these constraints?

A. Generative AI
B. Neural Networks
C. Ensemble models
D. Naive Bayes

Answer: D

Explanation:
Naive Bayes is a simple, efficient algorithm well-suited for small datasets and straightforward classification tasks like predicting customer repurchase likelihood.

NEW QUESTION # 45
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