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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| Trustworthy AI | 9% | - Transparency and explainability - Ethical considerations and bias - Privacy and security |
| Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| CPMAI Methodology | 41% | - Phase VI: Iteration & Monitoring
|
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NEW QUESTION # 291
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?
Answer: D
Explanation:
Overfitting occurs when a model is too sensitive to training data fluctuations, capturing noise instead of general patterns, which reduces its ability to generalize to new data.
NEW QUESTION # 292
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Choose all that apply.)
Answer: A,B,C,D,F
Explanation:
Generative AI is well-suited for augmenting human tasks, creating content, augmenting training data, automating content generation programmatically, and powering virtual avatars. Explainable decision-support typically involves different AI techniques focused on transparency rather than generation.
NEW QUESTION # 293
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?
Answer: D
NEW QUESTION # 294
A hospital system has been using a chatbot and has received complaints from end users. The end users believe they are speaking to a person but are frustrated when answers do not make sense. To help ensure end users know that they are engaging with an AI chatbot, what should be considered to support transparency?
Answer: C
Explanation:
A disclosure notice with each use clearly informs users that they are interacting with an AI chatbot rather than a human. This supports transparency, manages expectations, and helps reduce user frustration when responses are limited or unclear.
NEW QUESTION # 295
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 # 296
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