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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Define AI business objectives 2. Feasibility and ROI analysis
Topic 2: AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Topic 3: Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
Topic 4: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
Topic 5: Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Topic 6: 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 (Q130-Q135):

NEW QUESTION # 130
Clean, well-labeled, datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Split by patterned subsampling
B. Split by alphabetical order
C. Use the same data for all sets
D. Split by random subsampling

Answer: D

Explanation:
Random subsampling ensures that training, validation, and test sets are representative and unbiased samples of the overall data, which helps the model generalize well and provides reliable evaluation.

NEW QUESTION # 131
An AI recommendation system has been in production for four months. The project manager wants to ensure the solution continues to deliver business value.
What is the most effective mechanism?

A. Compare monthly model accuracy against the original evaluation results
B. Schedule an annual model audit
C. Survey users quarterly about their satisfaction with recommendations
D. Establish a continuous feedback loop that captures outcomes and feeds monitoring and retraining decisions

Answer: D

Explanation:
A feedback loop captures actual outcomes from production use and returns them to the monitoring and retraining process. This closes the CPMAI lifecycle, allowing the solution to be measured against business results and improved through subsequent iterations.

NEW QUESTION # 132
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?

A. Make sure the model lives on the edge device so it can be used regardless of internet connection
B. Make sure the model is available over a cloud-based API
C. Make sure the model lives in a hybrid environment
D. Make sure that you can use Generative AI solutions on an edge device

Answer: A

Explanation:
For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.

NEW QUESTION # 133
A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?

A. Ensuring diverse training datasets
B. Using interpretable models for decision-making
C. Implementing fairness-aware algorithms
D. Conducting bias audits during development

Answer: D

Explanation:
Conducting bias audits during development directly supports an ethical risk assessment by systematically testing whether the AI system produces biased outcomes before it is deployed.
This helps identify, measure, and address bias risks early in the diagnostic workflow.

NEW QUESTION # 134
A project team is tasked with developing an AI system to optimize workflow efficiency by automating both cognitive and noncognitive tasks. The AI system will handle various tasks including data analysis and decision-making to routine administrative functions. The AI system needs to be integrated with the existing enterprise software and meet international regulations.
The team needs to manage data from multiple sources with different formats and standards. The AI system needs to evolve as business processes and technologies change. How would the results for the system be different if cognitive and noncognitive tasks are not separated?

A. Complex algorithms would be applied to simple tasks.
B. Cognitive tasks could underperform due to overloading.
C. The AI system would have slower processing times.
D. There would be inefficiencies in handling repetitive task processes.

Answer: A

Explanation:
When cognitive and noncognitive tasks are not separated, the solution may apply advanced AI methods to simple rule-based or repetitive work. This creates unnecessary complexity, higher cost, and inefficient use of AI capabilities that should be reserved for tasks requiring reasoning, learning, prediction, or decision support.

NEW QUESTION # 135
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