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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
| 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 |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
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NEW QUESTION # 31
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 # 32
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled. Which phase of CPMAI is this done?
Answer: A
Explanation:
Phase II of CPMAI covers data preparation activities, including data labeling, to ensure the dataset is ready for model training.
NEW QUESTION # 33
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?
Answer: C
NEW QUESTION # 34
During development of a healthcare AI diagnostic tool, stakeholders request improving model accuracy by using a complex black-box model, reducing interpretability. Regulatory requirements emphasize transparency in clinical decisions. How should the project manager respond to this request?
Answer: B
Explanation:
The PM must balance performance with regulatory explainability requirements before making a decision.
NEW QUESTION # 35
A financial services firm is assessing the success of a newly operationalized AI system for fraud detection. The project manager needs to evaluate the model against business key performance indicators. (KPIs). What is an effective method to help ensure the accuracy of this evaluation?
Answer: C
Explanation:
Utilizing a diverse set of validation techniques improves evaluation accuracy by checking the AI fraud detection system from multiple perspectives, such as model performance, business KPI alignment, false positive and false negative rates, and operational impact. This gives a more reliable assessment than relying on one metric or broad financial reporting alone.
NEW QUESTION # 36
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