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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Topic 2: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Topic 3: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 5: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?
A) Increasing the amount of regularization to prevent overfitting
B) Checking for issues in the data preprocessing pipeline that may have introduced noise
C) Analyzing the distribution of real world data for potential shifts
D) Reviewing recent changes made to the model's architecture and parameters
2. A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?
A) Create a comprehensive data catalog.
B) Establish a cross-departmental data sharing agreement.
C) Develop a data visualization dashboard.
D) Implement advanced data encryption techniques.
3. An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions. What is the best approach for handling this project?
A) Run it as a hybrid approach and some phases are run separately while other phases are combined together
B) Run it as one project, combining teams, data requirements, and project needs
C) Run each pattern in isolation, with separate teams
D) Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
4. A project team at an IT services company is developing an AI solution to enhance network security. They need to define the success criteria to help ensure the project achieves its desired outcomes. What should the project manager do to define the relevant success criteria?
A) Use key performance indicators (KPIs) for incident response times and threat detection rates.
B) Conduct a SWOT (strengths, weaknesses, opportunities, threats) analysis of the network infrastructure.
C) Implement machine learning (ML) algorithms for threat prediction.
D) Perform a detailed cost-benefit analysis of security investments.
5. A team is in the early stages of an AI project. They need to ensure they have the necessary data and technology to support AI solution development. What is the first step the project team should complete?
A) Assess the team's current AI and data expertise.
B) Outline the business objectives for the AI project.
C) Verify the availability and quality of the required data.
D) Identify the gaps and procure the needed tools.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
