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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| 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: 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 |
| 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. 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 deterministic then go with the AI project.
B) Decide if it's a statistics pattern. If it's statistical then go with the AI project.
C) Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
D) See if simple rules work. If yes, then pick the right AI solution to solve the problem.
2. A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
A) Adopting a principle of least privilege for data access
B) Leveraging generative AI for advanced data analysis
C) Implementing a chain-of-thought prompting technique
D) Using a rule-based approach to maintain simplicity
3. A company needs to launch an AI application quickly to be the first to the market. The project team has decided to use pretrained models for their current AI project iteration. What is a key result of leveraging pretrained models?
A) The custom project development time can increase due to adjustments.
B) The team can encounter compatibility issues with existing systems.
C) The team can see a reduction in the overall project timeline.
D) The project can face unexpected scalability challenges.
4. A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility. Which method should the project manager use?
A) Leveraging natural language processing (NLP) to standardize patient records
B) Integrating electronic health records (EHR) with AI through machine learning (ML) algorithms
C) Employing a hybrid cloud strategy for scalable data storage
D) Performing a data quality assessment with extraction, transformation, and loading (ETL) processes
5. A project manager is preparing a contingency plan for an AI-driven customer service platform.
They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager's objective?
A) Developing an automated fallback chatbot with limited capabilities
B) Providing extensive training to customer service representatives on handling AI failures
C) Creating a robust customer service logging system to quickly identify and resolve issues
D) Implementing a manual override system for critical customer queries
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |
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