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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability |
| Topic 3: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 4: Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
| Topic 5: Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
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NEW QUESTION # 197
An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?
Answer: D
Explanation:
Demonstrating the use of bias detection tools directly addresses ethical concerns by showing how the project team evaluates fairness, identifies potentially discriminatory outcomes, and supports responsible operationalization of the AI model.
NEW QUESTION # 198
Accompany wants to determine whether to move forward with an AI project. The project manager has answered and received approval for all questions on technology, data, and business feasibility. Which activity did the project manager perform?
Answer: B
Explanation:
A go/no-go assessment evaluates whether the AI project should proceed by confirming technology readiness, data feasibility, and business value. Approval across these areas indicates the project manager completed the decision checkpoint needed to move forward.
NEW QUESTION # 199
A healthcare organization is evaluating the operationalization of an AI-powered diagnostic tool.
The tool is expected to enhance diagnostic accuracy and speed, thereby reducing patient wait times. The project manager needs to prepare a business case to present to the board of directors. Which method will help to ensure a robust business case?
Answer: B
Explanation:
Performing a cost-effectiveness study helps quantify whether the AI diagnostic tool's expected improvements in accuracy, speed, and reduced patient wait times justify the implementation costs. This provides a strong financial and operational basis for the board's business case decision.
NEW QUESTION # 200
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?
Answer: D
Explanation:
An iterative approach allows the team to begin with the available data while revisiting earlier project activities as additional data becomes accessible. This supports progress without assuming that every data dependency must be resolved before the forecasting project can start.
NEW QUESTION # 201
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?
Answer: A
Explanation:
PMI describes Predictive analytics & decision support as the AI pattern/capability that uses data- driven learning to anticipate outcomes and inform decisions, including "optimizing resource allocation." Route optimization for pizza delivery is fundamentally a decision-support problem: the organization is using historical and real-time signals (orders, traffic, distance, time windows) to recommend an improved routing plan that minimizes time, cost, or late deliveries. PMI also notes that dynamic route optimization is a common example of "goal-driven systems," often associated with reinforcement learning. However, since "goal-driven systems" is not one of the available answer choices, the closest PMI-aligned option among those provided is Predictive analytics, because it directly supports operational decisions under uncertainty and can continuously improve recommendations as more data becomes available. In CPMAI terms, the project manager should ensure the chosen capability matches the business need (faster deliveries, fewer miles, improved SLA performance) and define measurable success criteria for route recommendations and on-time delivery performance.
NEW QUESTION # 202
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