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PMI CPMAI Exam Syllabus Topics:

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

1. A government agency is operationalizing a new AI tool for predictive policing. The project manager needs to identify data subject matter experts (SMEs) to ensure data quality and relevance. The project team has access to historical crime data, socioeconomic data, and real- time incident reports. Which method will help in determining the data SMEs for this project?

A) Identifying individuals who have worked on similar AI tools in policing
B) Reviewing certifications in advanced data analytics and machine learning
C) Evaluating the team's familiarity with historical crime and socioeconomic data
D) Conducting workshops to assess knowledge in real-time incident data processing

2. A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?

A) Perform a comprehensive hyperparameter tuning.
B) Implement additional data augmentation techniques.
C) Establish continuous monitoring and feedback loops.
D) Set up cross-validation with a larger dataset.

3. An AI project team has identified a gap in their data knowledge and experience. They need to address this issue in order to proceed with their AI implementation. What is the effective solution?

A) Hire an external data consultant to provide targeted guidance and training.
B) Engage in a comprehensive data immersion program to build internal capabilities.
C) Deploy an adaptive data knowledge framework (ADKF) to bridge the expertise gap.
D) Utilize an AI-specific data enhancement protocol to improve data quality.

4. During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?

A) Model tuning
B) Model Selection
C) Model training
D) Algorithm Selection

5. Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

A) Real-time prediction
B) Batch prediction
C) Stream learning
D) Web service / Microservice

Solutions: