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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 9% | - Privacy and security - Ethical considerations and bias - Transparency and explainability |
| Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| Managing AI | 8% | - Risk management in AI projects - Stakeholder management - Managing AI project teams and resources |
| Machine Learning | 13% | - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) |
| AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
| CPMAI Methodology | 41% | - Phase IV: Model Evaluation
|
>> CPMAI Trustworthy Exam Torrent <<
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NEW QUESTION # 158
A retail bank wants to reduce fraudulent transactions by detecting unusual card activity in near real time. Which AI capability should be used?
Answer: A
Explanation:
PMI's Seven Patterns of AI describes Predictive analytics & decision support as using data-driven learning to anticipate outcomes and support decisions under uncertainty. Fraud detection is a classic predictive use case: the system analyzes historical and current transaction behaviors to estimate the probability of fraud and recommend actions (approve, decline, escalate). In CPMAI- aligned delivery, the project manager ensures the AI capability matches the business objective and defines measurable performance metrics and thresholds (e.g., false positives, fraud loss reduction, detection latency). PMI-CPMAI also emphasizes responsible and trustworthy AI practices--particularly around privacy, governance, and monitoring--because fraud models can affect customers' access to funds and may introduce bias if training data is skewed. Predictive analytics best fits because it supports classification/risk scoring decisions; the other options focus on interaction (conversational), tailored experiences (hyperpersonalization), or self-directed control (autonomous systems).
NEW QUESTION # 159
An organization is planning their digital transformation initiatives by building an AI solution to focus on data-collection needs. The goal is to reduce the manual handling of data. Which approach should be prioritized to achieve the objective?
Answer: A
Explanation:
Implementing intelligent systems that can autonomously process and analyze data directly reduces manual data handling. This supports the digital transformation goal by automating data collection, processing, and analysis through AI-enabled capabilities.
NEW QUESTION # 160
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
What role would be most suited for this?
Answer: C
NEW QUESTION # 161
A project manager overseeing an AI project is concerned about potential data breaches. They need to establish a robust privacy and security plan. Where should the plan primarily focus to minimize risk?
Answer: B
Explanation:
A robust privacy and security plan should primarily establish stringent access control policies so that sensitive AI project data is accessible only to authorized users based on defined roles and permissions. This directly reduces the risk of unauthorized access and potential data breaches.
NEW QUESTION # 162
A project member is deciding on the appropriate AI algorithm for a new project. The goal is to achieve high accuracy while maintaining transparency in the decision-making process.
What should the project manager suggest?
Answer: C
Explanation:
Evaluating algorithm explainability helps ensure the selected model can provide understandable reasons for its predictions or decisions while still meeting performance requirements. This directly supports the project's need to balance accuracy with transparency.
NEW QUESTION # 163
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