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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q241-Q246):

NEW QUESTION # 241
In a logistics project, a company is integrating an AI-driven supply chain optimization tool. The project manager needs to address the risk of external dependencies affecting project success.
Which method is a next step for the project manager?

A. Developing a comprehensive risk matrix to monitor external dependencies
B. Establishing contingency plans for critical external dependencies
C. Negotiating long-term contracts with reliable third-party vendors
D. Conducting regular audits of third-party vendor performance and compliance

Answer: B

Explanation:
Establishing contingency plans for critical external dependencies helps the project manager prepare fallback actions if vendors, data providers, platforms, integrations, or infrastructure services create delays or disruptions. This directly reduces the impact of external dependency risks on the AI supply chain optimization project.

NEW QUESTION # 242
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
B. Collect enormous amounts of data - the more data the better.
C. Understanding which algorithms are best for your data needs.
D. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.

Answer: A

Explanation:
Having team members with the experience and skills to manage, process, and analyze massive volumes of data is crucial for leveraging big data effectively in AI projects. Proper expertise ensures data is handled correctly to drive meaningful insights and model performance.

NEW QUESTION # 243
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. Outsource the project ASAP
B. Find a citizen data scientist to help
C. Put the project on hold until the resources needed become available
D. Use an AutoML solution

Answer: D

Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.

NEW QUESTION # 244
An aerospace company is evaluating whether their sensor data meets the requirements for an AI- based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures. Which method addresses the requirements?

A. Analyzing data completeness and conducting feature engineering
B. Implementing a data governance framework to ensure compliance
C. Evaluating the data schema and integrating additional data sources
D. Performing a data quality assessment focusing on precision and latency

Answer: D

Explanation:
Performing a data quality assessment focused on precision and latency directly verifies whether the sensor data is accurate enough, captured at the right resolution, and available quickly enough to support reliable predictive maintenance decisions.

NEW QUESTION # 245
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

A. See if you can purchase the data needed to continue with the project
B. See if you can expand the scope to continue with the project
C. See if you can adjust the scope of this interaction to continue with the project
D. See if you already have access to enough data to continue with the project

Answer: C

NEW QUESTION # 246
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