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| Section | Weight | Objectives |
|---|---|---|
| Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions |
1. In a government healthcare AI project, the objective is to reduce patient wait times by optimizing staff schedules. After 6 months, the cost is US$500,000 with a completion rate of 60%. The project manager needs to determine the return on investment (ROI) to justify the current expenditure. What is an effective method to achieve this objective?
A) Calculate the total savings in patient wait times and compare them to the initial cost.
B) Utilize a net present value model to project future benefits.
C) Evaluate the incremental cost-benefit analysis using the cost-performance baseline.
D) Apply a cost-consequence analysis to measure project efficiency.
2. A project manager is evaluating whether an AI model is ready for operationalization. The project team has ensured the model meets accuracy and performance requirements and has conducted extensive testing. However, uncertainty about the model's operationalization in different environments and its compliance with regulatory standards still exists. What should the team do next?
A) Reassess the project's data preparation phase.
B) Consider retraining the model for better performance.
C) Evaluate the model's adaptability to diverse conditions.
D) Implement additional noncognitive solutions.
3. An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?
A) Lines of code written
B) Response time only
C) Number of features delivered
D) User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
4. Your team is working on an AI system to provide a more personalized experience for customers on your website. What should the team do in regard to determining the pattern of AI with regards to the ROI of the project?
A) First identify the AI pattern you want to use and then figure out the ROI
B) First identify the objective you're trying to solve or the ROI you desire and then use that to figure out that correct pattern
C) First determine the pattern of AI you want to use and then work with stakeholders to come up with ROI
D) First talk to senior managers who set the ROI of the project
5. 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) Performing a data quality assessment focusing on precision and latency
B) Analyzing data completeness and conducting feature engineering
C) Implementing a data governance framework to ensure compliance
D) Evaluating the data schema and integrating additional data sources
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
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