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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Topic 2: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| Topic 3: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact |
| Topic 4: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Topic 5: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy |
1. A manufacturing firm is planning to implement a network of intelligent machines to increase efficiency on the assembly line. The machines are equipped with advanced AI capabilities including precision assembly, quality control for predictive maintenance, and real-time data analysis. The intelligent machines should enhance operational efficiency, reduce downtime, and improve product quality. There needs to be seamless communication between the machines and existing systems, compliance with industry regulations, and a managed transition for the workforce. What is a beneficial outcome of using intelligent machines in this environment?
A) Over-reliance on technology leading to skill degradation
B) Scalability and flexibility in production
C) Higher investment costs without immediate returns
D) Increased vulnerability to cybersecurity threats
2. As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
A) Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
B) Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
C) Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
D) Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
3. Your team is looking for a short term ROI project and decides that an AI enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition. What's the best course of action with how to proceed?
A) Move forward with the project as planned. The problem definition will become clear later on in the project.
B) Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
C) Do not move forward until you can determine a clear problem definition.
D) Do not move forward and cancel the project all together.
4. You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)
A) Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
B) Strong math skills, especially in calculus and statistics
C) Automation skills, especially around creating RPA bots
D) Prompt engineering skills
E) Understanding of algorithms
F) Critical thinking skills
5. Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:
A) Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
B) See if simple rules work. If yes, then pick the right AI solution to solve the problem.
C) Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.
D) Decide if it's a statistics pattern. If it's statistical then go with the AI project.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: A,B,D,E,F | Question # 5 Answer: A |
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