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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Topic 2: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| Topic 3: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 4: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| Topic 5: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned. What would likely have helped avoid this conflict?
A) Nothing - running automation projects like autonomous projects is the correct thing to do.
B) Everyone on the team should understand the differences between automation and autonomous systems.
C) Senior management should become involved in the project.
D) Applying a hybrid approach of automation and AI best practices would have achieved better results.
2. During the business understanding phase, the project manager realizes that the project team has overlooked requirements for the AI model. The model should have a high level of explainability and transparency. What should the project manager do?
A) Revisit the training data for the smart algorithm.
B) Develop an algorithm to account for future model adjustments.
C) Define a process for data aggregation and analysis.
D) Ensure data compliance requirements are clearly documented.
3. You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?
A) Use of Generative Outpainting
B) Use of inpainting to replace image components
C) Use of super-resolution to enhance the existing image
D) Prompt engineering for new image generation
4. A capital markets firm is exploring the use of AI to enhance its trading algorithms. The firm expects the AI solution will increase trading accuracy and profitability. The project manager needs to create a business case to justify the AI investment. Which method will provide results that meet the firm's goals and objectives?
A) Conducting a market trend analysis.
B) Performing a scenario analysis
C) Developing a financial impact assessment
D) Consulting with AI vendors
5. An aerospace company is in the data preparation phase of an AI project. The project team must verify data quality to make a go/no-go decision for model development. They need to integrate data from several sensors with different sampling rates. What is an effective method that helps to ensure data consistency?
A) Aggregating sensor data
B) Utilizing data interpolation methods
C) Applying a real-time data synchronization protocol
D) Developing a custom data integration framework
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |

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