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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
| Topic 3: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
| Topic 4: 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 |
| Topic 5: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
1. Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration. What's wrong with this workflow?
A) Team members should not be able to move to new projects until senior management signs off
B) Model Evaluation requires continuous model evaluation, retraining, and operationalization
C) You need to make sure the AI Go/No Go questions have been addressed
D) Nothing is wrong with this workflow. You can move to the next iteration
2. 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) Everyone on the team should understand the differences between automation and autonomous systems.
B) Applying a hybrid approach of automation and AI best practices would have achieved better results.
C) Senior management should become involved in the project.
D) Nothing - running automation projects like autonomous projects is the correct thing to do.
3. An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?
A) As optional guidelines based on project scope
B) As part of the data preparation phase
C) As part of the final testing phase
D) As part of the business understanding phase
4. Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?
A) Increase model complexity
B) Allow teams full autonomy
C) Focus only on technical performance
D) Establish an AI governance framework and policies
5. Your team is tasked with selecting an algorithm for a supervised learning classification project.
Which algorithm might you choose?
A) K-nearest neighbor
B) Q learning
C) K-means
D) Gaussian mixture
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
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