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| Section | Weight | Objectives |
|---|---|---|
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| 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 |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
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NEW QUESTION # 259
An IT services company is working on a project to develop an AI-based customer support system.
During data preparation, the project manager needs to clean and transform customer interaction logs.
What is an effective technique to handle any missing data?
Answer: B
NEW QUESTION # 260
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: B
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 # 261
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?
Answer: B
NEW QUESTION # 262
An AI system recommends loan approval decisions. Regulators require that a qualified employee make the final determination and be able to override the system.
What control does this requirement describe?
Answer: D
Explanation:
Human-in-the-loop places a person in the decision path with authority to review, confirm, or override the system's output. This is a common requirement in regulated, high-consequence domains where full automation is not permitted.
NEW QUESTION # 263
A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation. What is represented in this situation?
Answer: A
Explanation:
Transparency is represented because the team is documenting AI decisions, rationale, impacts, and implementation issues so future reviewers can understand how and why choices were made.
This supports auditability, accountability, and traceability across the AI project.
NEW QUESTION # 264
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