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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI Model Development and Iteration | - Model building and validation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
An IT services company is operationalizing an AI-based customer service chatbot. The project manager needs to track the workflow of key resources such as natural language processing experts, cloud computing resources, and integration specialists. What will enable the project manager to visualize and manage the cadence of resource activities?
- A. Implementing a balanced scorecard
- B. Conducting a root cause analysis
- C. Applying a critical path method
- D. Using a kanban board
Correct Answer: D 🗳️
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A project involves integrating AI systems across multiple departments, each with different access levels. This complex Al project has presented the project manager with significant issues related to data misuse. The project team has been focused on their ethics guidelines but continues to experience data misuse. The project involves different regional data protection regulations which further increases the complexity. What issue will cause these challenges to occur?
- A. Failure to implement robust encryption for data security
- B. Lack of a detailed plan addressing a governance strategy
- C. Overlooking algorithmic bias and fairness concerns
- D. Limited awareness of explainability requirements
Correct Answer: B 🗳️
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A hospital system has been using a chatbot and has received complaints from end users. The end users believe they are speaking to a person but are frustrated when answers do not make sense. To help ensure end users know that they are engaging with an AI chatbot, what should be considered to support transparency?
- A. Disclosure notice with each use
- B. Use of interpretable AI models
- C. Operationalize advanced algorithms
- D. Inclusion of diverse data sets
Correct Answer: A 🗳️
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A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?
- A. Limiting the data sources to internal databases to avoid complications
- B. Conducting a comprehensive data audit and cleansing process
- C. Using pretrained models without tailoring to specific data
- D. Integrating data without improvement checks to expedite the project timeline
Correct Answer: B 🗳️
A fraud detection model is evaluated on a dataset in which 0.4 percent of transactions are fraudulent. The model reports 99.6 percent accuracy.
How should the project manager interpret this result?
- A. The model requires additional training epochs to improve accuracy
- B. The model meets the accuracy threshold and can be operationalized
- C. The evaluation dataset is too small to support a conclusion
- D. Accuracy is misleading for this distribution; precision and recall must be assessed
Correct Answer: D 🗳️
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