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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 - Data strategy and governance - DataOps concepts |
| Managing AI | 8% | - Stakeholder management - Risk management in AI projects - Managing AI project teams and resources |
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning |
| CPMAI Methodology | 41% | - Phase II: Data Identification & Curation
|
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NEW QUESTION # 160
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 # 161
A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?
Answer: B
Explanation:
Conducting a comprehensive data audit and cleansing process verifies whether internal and external fraud-related data is accurate, complete, relevant, and usable for the AI solution before model development or operationalization.
NEW QUESTION # 162
A telecommunications company is implementing an AI solution to optimize network performance.
The project team needs to prepare the data for the AI system by addressing data format inconsistencies. Which method should the project manager use?
Answer: C
Explanation:
PMI's CPMAI/PMI-CPMAI guidance places "data preparation and transformation" at the center of getting data into a usable state for model development and operations. The CPMAI v7 outline explicitly includes coordinating data preparation activities such as formulating data preparation requirements and performing data cleansing and enhancement--work that directly addresses inconsistent formats. In addition, CPMAI v7 lists "Executing Data Preparation and Transformation," including methods to improve data quality and accuracy and to clean/enhance data for optimal AI performance. When the issue is format inconsistency (e.g., mismatched schemas, units, encodings, timestamp formats), the PMI-aligned response is to define and execute the required transformation steps (normalize formats, standardize fields, convert units, align timestamps, encode categories) so the dataset meets the model and pipeline requirements.
NEW QUESTION # 163
An aerospace company is developing an AI tool for anomaly detection in aircraft engines.
Identifying data sources and their locations is critical. Which method will help to ensure the project meets these objectives?
Answer: D
Explanation:
Establishing a metadata management system helps document where aircraft engine data comes from, where it is stored, what it represents, who owns it, and how it should be used. This supports accurate identification and location tracking of the data sources needed for anomaly detection.
NEW QUESTION # 164
An energy technology company needs to convey data understanding to the leadership team for an AI project aimed at predicting equipment failures. The project involves analyzing sensor data from various equipment. Which necessary initial task should the project manager take?
Answer: D
Explanation:
Developing a preprocessing pipeline is the necessary initial task because sensor data must be cleaned, normalized, and prepared before it can be reliably analyzed, understood, or used to predict equipment failures.
NEW QUESTION # 165
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