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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Model Development and Iteration | - Model building and validation
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 30
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 # 31
An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?
Answer: B
Explanation:
Differences between training and production data distributions cause performance degradation.
NEW QUESTION # 32
An AI-powered chatbot occasionally generates inappropriate or harmful responses despite strong training performance. The organization wants to reduce risks while maintaining usability. What is the most effective mitigation strategy for this issue?
Answer: B
Explanation:
Guardrails control outputs and reduce harmful responses.
NEW QUESTION # 33
A project team is tasked with developing an AI system to optimize workflow efficiency by automating both cognitive and noncognitive tasks. The AI system will handle various tasks including data analysis and decision-making to routine administrative functions. The AI system needs to be integrated with the existing enterprise software and meet international regulations.
The team needs to manage data from multiple sources with different formats and standards. The AI system needs to evolve as business processes and technologies change. How would the results for the system be different if cognitive and noncognitive tasks are not separated?
Answer: C
Explanation:
When cognitive and noncognitive tasks are not separated, the solution may apply advanced AI methods to simple rule-based or repetitive work. This creates unnecessary complexity, higher cost, and inefficient use of AI capabilities that should be reserved for tasks requiring reasoning, learning, prediction, or decision support.
NEW QUESTION # 34
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions. What is the best approach for handling this project?
Answer: D
Explanation:
A hybrid approach allows flexibility by running some phases separately and others together, accommodating the unique requirements of multiple AI patterns within one overall project.
NEW QUESTION # 35
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