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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Topic 2: Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Topic 3: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning |
| Topic 4: Trustworthy AI | 9% | - Transparency and explainability - Ethical considerations and bias - Privacy and security |
| Topic 5: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 6: Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
A) Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
B) Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
C) Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
D) Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
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) Establish continuous monitoring and feedback loops.
C) Implement additional data augmentation techniques.
D) Set up cross-validation with a larger dataset.
3. A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality. Which method will meet the project team's objectives?
A) Using data profiling tools to assess data completeness
B) Setting up a continuous integration pipeline for real-time data validation
C) Operationalizing a data catalog to maintain metadata standards
D) Implementing data augmentation techniques to fill missing values
4. You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?
A) Dimensionality Reduction
B) Gradient Descent
C) Data selection
D) Multivariate regression
5. Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected. What could have been done better to get the team to feel comfortable with this technology and use it? (Choose all that apply.)
A) Have upper management relay to employees this tool is to augment, and not replace their jobs.
B) Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.
C) Have the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
D) Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: A,B,D |




