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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
Topic 2: AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Topic 3: AI Model Development and Iteration		- Model building and validation 1. Machine learning / generative AI model development 2. Iterative delivery approach
Topic 4: Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Topic 5: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
Topic 6: Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q317-Q322):

NEW QUESTION # 317
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn't have access to all the data it needs.
What's the best course of action?

A. Cautiously move forward knowing you may need to pause mid-project which is ok.
B. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
C. Do not move forward until you have access to all the data you need.
D. Move ahead as planned so you stay on time with your project.

Answer: C

NEW QUESTION # 318
A logistics company is developing an AI system to manage and predict supply chain disruptions.
The project manager needs to evaluate the ethical implications of using this AI system. Which action will help to ensure ethical use?

A. Establishing a centralized data repository
B. Adopting a hybrid cloud architecture
C. Implementing blockchain for data transparency
D. Conducting an algorithmic impact assessment

Answer: D

Explanation:
Conducting an algorithmic impact assessment helps evaluate the ethical risks, potential harms, fairness concerns, transparency issues, and accountability requirements of the AI system before and during use in supply chain disruption management.

NEW QUESTION # 319
Accompany wants to determine whether to move forward with an AI project. The project manager has answered and received approval for all questions on technology, data, and business feasibility. Which activity did the project manager perform?

A. Operationalized a pilot version
B. Developed initial machine learning models
C. Data collection and aggregation
D. Conducted a go/no-go assessment

Answer: D

Explanation:
A go/no-go assessment evaluates whether the AI project should proceed by confirming technology readiness, data feasibility, and business value. Approval across these areas indicates the project manager completed the decision checkpoint needed to move forward.

NEW QUESTION # 320
A healthcare AI model achieves high predictive accuracy but lacks transparency in decision- making. Regulators require clear explanations for each prediction to ensure patient safety and trust. What is the primary risk of deploying this model as-is?

A. Data duplication issues
B. Reduced system speed
C. Increased operational costs
D. Regulatory non-compliance

Answer: D

Explanation:
Lack of explainability may violate healthcare regulations and safety standards.

NEW QUESTION # 321
A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data. If the project manager avoids addressing the variety of data during preparation, what will be the result?

A. Reduced model performance
B. Decreased data processing speed
C. Increased data consistency
D. Improved model accuracy

Answer: A

Explanation:
Failing to address the variety of structured and unstructured data can leave the model with inconsistent formats, incomplete representations, and unusable inputs. This reduces the model's ability to learn accurate fleet management patterns and leads to weaker performance.

NEW QUESTION # 322
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