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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | 13% | - DataOps concepts - Data strategy and governance - Data preparation and preprocessing |
| Topic 2: Trustworthy AI | 9% | - Transparency and explainability - Ethical considerations and bias - Privacy and security |
| Topic 3: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning |
| Topic 4: CPMAI Methodology | 41% | - Phase VI: Iteration & Monitoring
|
| Topic 5: Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
| Topic 6: Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) |
>> PMI CPMAI Test Questions Vce <<
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NEW QUESTION # 319
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)
Answer: A,C
Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.
NEW QUESTION # 320
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?
Answer: D
Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.
NEW QUESTION # 321
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?
Answer: D
Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.
NEW QUESTION # 322
A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains.
Which method will help identify the qualified data SMEs?
Answer: D
Explanation:
PMI-CPMAI distinguishes clearly between different types of expertise needed in an AI project:
AI/ML specialists, data specialists (data SMEs), domain SMEs, and security or infrastructure experts. When the question specifically asks about data subject matter experts (SMEs), the focus is on people who deeply understand how the organization's data is structured, stored, accessed, and governed.
For an AI-powered anomaly detection tool in a government data security context, qualified data SMEs are those who know the existing data architectures, logging systems, data flows, schemas, and constraints. They can explain where relevant data resides (e.g., network logs, access records, system events), how it is currently managed and protected, and what limitations or quality issues may affect AI performance. Evaluating candidates on their expertise with existing data architectures and their ability to optimize databases directly targets this competency.
Knowledge of neural networks, hyperparameter tuning, or GANs is more characteristic of AI/ML engineers, not data SMEs. PMI-CPMAI guidance emphasizes that AI success depends on the right mix of roles, and data SMEs are vital for defining data requirements, ensuring data suitability, and aligning with security and governance standards. Therefore, the method that best identifies the appropriate data SMEs for this anomaly detection project is to evaluate their expertise with current data architectures and their ability to optimize and manage those data systems.
NEW QUESTION # 323
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?
Answer: D
Explanation:
Exploratory data analysis helps verify whether the server downtime data is complete, accurate, consistent, relevant, and suitable before moving into data preparation. It allows the project manager to identify missing values, outliers, patterns, anomalies, and quality issues that could affect model development.
NEW QUESTION # 324
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