PMI - CPMAI - Professional Latest Cognitive Project Management in AI (PMI-CPMAI) Exam Bootcamp

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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI
Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Define AI business objectives 2. Feasibility and ROI analysis
Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q26-Q31):

NEW QUESTION # 26
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. Only document model performance once at launch
B. Allow each team to apply its own data definitions
C. Establish data governance and supplier controls, including auditability and monitoring
D. Remove all external data sources immediately

Answer: C

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 # 27
A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model's predictions are biased toward a particular class.
What is an effective technique to mitigate this bias?

A. Using synthetic data generation
B. Increasing the batch size
C. Implementing stratified sampling
D. Adjusting the hyperparameters

Answer: C

Explanation:
Stratified sampling preserves appropriate class proportions when creating training or evaluation subsets. This helps ensure that each class is adequately represented, reducing class-related sampling bias and producing a more balanced assessment of the churn model.

NEW QUESTION # 28
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled. Which phase of CPMAI is this done?

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

Answer: F

Explanation:
Phase II of CPMAI covers data preparation activities, including data labeling, to ensure the dataset is ready for model training.

NEW QUESTION # 29
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions. What is the best approach for handling this project?

A. Run each pattern in isolation, with separate teams
B. Run it as a hybrid approach and some phases are run separately while other phases are combined together
C. Run it as one project, combining teams, data requirements, and project needs
D. Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs

Answer: B

Explanation:
A hybrid approach allows flexibility by running some phases separately and others together, accommodating the unique requirements of multiple AI patterns within one overall project.

NEW QUESTION # 30
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project - AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?

A. Determine the long-term need for the organization and build the project to that long-term goal.
B. Evaluate whether the solution can be done with automation.
C. Determine if the project fits within the scope, budget, and timeline set out.
D. Determine whether the project has a cognitive technology component and meets a short-term need.

Answer: D

NEW QUESTION # 31
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