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Last Updated: Aug 02, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| 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: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact |
| Topic 5: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
1. A capital markets firm is exploring the use of AI to enhance its trading algorithms. The firm expects the AI solution will increase trading accuracy and profitability. The project manager needs to create a business case to justify the AI investment. Which method will provide results that meet the firm's goals and objectives?
A) Developing a financial impact assessment
B) Consulting with AI vendors
C) Conducting a market trend analysis.
D) Performing a scenario analysis
2. You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?
A) Find a citizen data scientist to help
B) Outsource the project ASAP
C) Put the project on hold until the resources needed become available
D) Use an AutoML solution
3. A logistics company wants to use AI to optimize delivery routes for a client that runs a pizza franchise. Which AI capability should be used?
A) Predictive analytics
B) Hyperpersonalization
C) Conversational
D) Autonomous systems
4. An aerospace company is integrating AI into their manufacturing process to enhance safety and efficiency. The project team needs to evaluate potential security threats to prevent unauthorized access to sensitive data. What is the highest risk?
A) Secure APIs and data flows by enforcing data governance
B) Implementing an AI model without regular data updates
C) Employing a proprietary software with no open-source review
D) Operationalizing a decentralized data storage system
5. A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?
A) Emphasizing the simplicity and reliability of noncognitive solutions
B) Conducting a cost-benefit analysis comparing AI and noncognitive solutions
C) Focusing on the novelty and technological AI appeal
D) Relying only on industry trends favoring AI adoption
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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