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PMI CPMAI Exam Syllabus Topics:

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

1. A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low. What is the cause for the precision issue?

A) The feature selection process is flawed.
B) The model is overfitting the training data.
C) The training data is unbalanced.
D) The model is underfitting the validation data.

2. A project manager is preparing a contingency plan for an AI-driven customer service platform.
They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager's objective?

A) Implementing a manual override system for critical customer queries
B) Creating a robust customer service logging system to quickly identify and resolve issues
C) Developing an automated fallback chatbot with limited capabilities
D) Providing extensive training to customer service representatives on handling AI failures

3. A manufacturing firm is planning to implement a network of intelligent machines to increase efficiency on the assembly line. The machines are equipped with advanced AI capabilities including precision assembly, quality control for predictive maintenance, and real-time data analysis. The intelligent machines should enhance operational efficiency, reduce downtime, and improve product quality. There needs to be seamless communication between the machines and existing systems, compliance with industry regulations, and a managed transition for the workforce. What is a beneficial outcome of using intelligent machines in this environment?

A) Scalability and flexibility in production
B) Higher investment costs without immediate returns
C) Increased vulnerability to cybersecurity threats
D) Over-reliance on technology leading to skill degradation

4. You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A) If this data is not needed, use Data anonymization techniques to remove it before feeding to models
B) Use noise reduction techniques to reduce all forms of data noise
C) Store the data in a data warehouse
D) Implement a new data privacy policy

5. You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?

A) Find a citizen data scientist to help
B) Put the project on hold until the resources needed become available
C) Use an AutoML solution
D) Outsource the project ASAP

Solutions: