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PMI CPMAI Exam Syllabus Topics:

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

1. Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing illnesses. The AI system is integrated with several medical databases. This allowed the AI system to learn from new patient data and adapt to the latest medical knowledge and practices. The final project report indicated that the AI model had degraded over time which impacted reliability and effectiveness. The AI system must comply with healthcare regulations from various countries.
What is the likely cause for the degradation issue?

A) Inadequate initial model validation
B) Data drift affecting model precision
C) Changes in business model requirements
D) Impact of data drift on model accuracy

2. Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?

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

3. A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A) Performing systematic data validation
B) Applying predictive data algorithms
C) Enhancing with data augmentation techniques
D) Implementing an AI data needs assessment

4. A project member is deciding on the appropriate AI algorithm for a new project. The goal is to achieve high accuracy while maintaining transparency in the decision-making process.
What should the project manager suggest?

A) Select an algorithm known for high accuracy in similar projects.
B) Ensure the selected algorithm complies with industry standards.
C) Consult with stakeholders about their transparency requirements.
D) Evaluate the explainability of potential algorithms.

5. A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis.
Which action best addresses the problem during data preparation?

A) Ignore the inconsistency because the model will learn patterns anyway
B) Skip validation to save time
C) Determine and apply data transformation and standardization steps
D) Replace real data with only synthetic data

Solutions: