CPMAI Minimum Pass Score | Exam CPMAI Overview

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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI
Topic 2: Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
Topic 3: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
Topic 4: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Topic 5: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
Topic 6: Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q54-Q59):

NEW QUESTION # 54
During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?

A. Reviewing recent changes made to the model's architecture and parameters
B. Increasing the amount of regularization to prevent overfitting
C. Checking for issues in the data preprocessing pipeline that may have introduced noise
D. Analyzing the distribution of real world data for potential shifts

Answer: D

NEW QUESTION # 55
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

A. See if you can adjust the scope of this interaction to continue with the project
B. See if you can purchase the data needed to continue with the project
C. See if you already have access to enough data to continue with the project
D. See if you can expand the scope to continue with the project

Answer: A

NEW QUESTION # 56
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn't have access to all the data it needs.
What's the best course of action?

A. Do not move forward until you have access to all the data you need.
B. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
C. Cautiously move forward knowing you may need to pause mid-project which is ok.
D. Move ahead as planned so you stay on time with your project.

Answer: A

NEW QUESTION # 57
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

A. Data Acquisition / Ingest / Capture
B. ELT Pipeline
C. Feature Engineering
D. Retraining Pipelines

Answer: D

NEW QUESTION # 58
A healthcare project manager is evaluating whether to implement an AI-powered diagnostic tool.
The initial cost is US$500,000 with an expected return on investment (ROI) of 15% within the first year. The project needs to satisfy multiple stakeholders including hospital administrators and medical staff.
Which method will maximize a positive ROI for the AI implementation?

A. Seeking verbal commitments from interested parties at each project phase
B. Ensuring all AI and non-AI components are integrated seamlessly
C. Acquiring alternatives to the AI solution as a contingency plan
D. Monitoring AI model performance against key performance indicators

Answer: D

NEW QUESTION # 59
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