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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q220-Q225):

NEW QUESTION # 220
A company needs to launch an AI application quickly to be the first to the market. The project team has decided to use pretrained models for their current AI project iteration. What is a key result of leveraging pretrained models?

A. The custom project development time can increase due to adjustments.
B. The project can face unexpected scalability challenges.
C. The team can encounter compatibility issues with existing systems.
D. The team can see a reduction in the overall project timeline.

Answer: D

Explanation:
Leveraging pretrained models can reduce the overall project timeline because the team starts from an existing model that has already learned general patterns. This can shorten development, training, and experimentation time for the current AI project iteration.

NEW QUESTION # 221
A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?

A. Establish data governance and supplier controls, including auditability and monitoring
B. Allow each team to apply its own data definitions
C. Remove all external data sources immediately
D. Only document model performance once at launch

Answer: A

Explanation:
PMI's trustworthy AI framing highlights governance, transparency, and accountability as essential ingredients for systems people can interpret and monitor. When third-party data feeds can change, the PMI-aligned approach is to establish governance and supplier controls that define data quality expectations, lineage, permitted uses, privacy constraints, and monitoring/audit mechanisms. This supports accountability by making data dependencies explicit and enabling early detection when upstream changes degrade model behavior.

NEW QUESTION # 222
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

A. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
B. Make sure all senior leadership is data literate, understands the Vs of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
C. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
D. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

Answer: B

NEW QUESTION # 223
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. Have upper management relay to employees this tool is to augment, and not replace their jobs.
B. Have the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
C. 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.
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,C,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 # 224
An AI initiative has demonstrated a technically successful model, but the business unit cannot quantify what the improvement is worth.
What was most likely omitted during Business Understanding?

A. A model selection rationale
B. A stakeholder register
C. A data acquisition plan
D. Baseline performance measurement of the current process

Answer: D

Explanation:
Business value is calculated as the difference between the AI-enabled outcome and the existing baseline. Without measuring current process performance before the project, improvements cannot be quantified or attributed to the AI solution.

NEW QUESTION # 225
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