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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 2: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 3: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 4: Data for AI | - Data identification and governance
| |
| Topic 5: AI Model Development and Iteration | - Model building and validation
| |
| Topic 6: AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 242
An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?
Answer: C
Explanation:
When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.
NEW QUESTION # 243
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?
Answer: A
NEW QUESTION # 244
Your team is looking for a short term ROI project and decides that an AI enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition. What's the best course of action with how to proceed?
Answer: D
Explanation:
A clear problem definition is essential before moving forward; without it, the project risks misalignment with business goals and wasted resources, so progress should be paused until clarity is achieved.
NEW QUESTION # 245
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: B,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 # 246
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: B
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 # 247
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