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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact |
| Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?
A) Data preparation
B) Data Governance
C) Data Integration
D) Data Mining or Data Analytics
2. A financial services firm is building an AI model to detect fraudulent transactions. Identifying and validating data sources is critical to the model's success. What is an effective method that helps to ensure data accuracy?
A) Employing a federated database system for decentralized data access
B) Setting up a batch processing system for data cleansing
C) Utilizing data lineage tools to track data origin and transformations
D) Implementing a blockchain-based ledger for transaction data
3. A manufacturing company is operationalizing an AI-driven quality control system. The project manager needs to ensure data privacy and regulatory compliance due to the critical nature in protecting sensitive operational data. What is an effective technique that addresses these requirements?
A) Implementing a zero-trust architecture for network security
B) Using a hybrid encryption scheme for storage
C) Applying data anonymization to the dataset
D) Utilizing a secure multiparty computation framework
4. An aerospace company is evaluating whether their sensor data meets the requirements for an AI- based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures. Which method addresses the requirements?
A) Implementing a data governance framework to ensure compliance
B) Analyzing data completeness and conducting feature engineering
C) Evaluating the data schema and integrating additional data sources
D) Performing a data quality assessment focusing on precision and latency
5. A government health agency is considering the implementation of AI to predict disease outbreaks. The project manager needs to determine whether AI is the right solution to address their needs. The solution must analyze large datasets from various regions and provide accurate predictions without overwhelming the system. In addition, the agency has non-Al alternatives such as traditional statistical models. What is a necessary initial task the project manager should perform?
A) Identifying which parts of the problem require cognitive solutions
B) Assessing the agency's current technological infrastructure
C) Evaluating whether the AI system can scale effectively
D) Determining the data sources and their reliability
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A |
