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PMI CPMAI Exam Syllabus Topics:

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

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Free PDF Quiz CPMAI - Cognitive Project Management in AI (PMI-CPMAI) –High Pass-Rate Reliable Dumps Sheet

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q313-Q318):

NEW QUESTION # 313
A deployed AI system meets all technical acceptance criteria, but six weeks after release, usage data shows that fewer than 10 percent of intended users are engaging with it.
What was most likely inadequate?

A. Data quality assessment
B. Model evaluation rigor
C. Infrastructure capacity planning
D. Organizational change management and user adoption planning

Answer: D

Explanation:
Technical success does not produce business value unless users adopt the system in their workflow. Adoption requires training, workflow integration, communication, and management of concerns about how the system affects existing roles.

NEW QUESTION # 314
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

A. Understanding of algorithms
B. Strong math skills, especially in calculus and statistics
C. Automation skills, especially around creating RPA bots
D. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
E. Prompt engineering skills
F. Critical thinking skills

Answer: A,B,D,F

NEW QUESTION # 315
A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?

A. Remove all external data sources immediately
B. Only document model performance once at launch
C. Establish data governance and supplier controls, including auditability and monitoring
D. Allow each team to apply its own data definitions

Answer: C

Explanation:
PMI's trustworthy AI framing highlights governance, transparency, and accountability as essential ingredients for systems people can interpret and monitor. When third-party data feeds can change, the PMI-aligned approach is to establish governance and supplier controls that define data quality expectations, lineage, permitted uses, privacy constraints, and monitoring/audit mechanisms. This supports accountability by making data dependencies explicit and enabling early detection when upstream changes degrade model behavior.

NEW QUESTION # 316
A logistics company is planning to separate cognitive and noncognitive parts of an AI project.
They need to manage teams working on these parts. What is a necessary initial task?

A. Implementing a customer-driven feedback loop in planning
B. Utilizing a project management software to monitor progress
C. Developing a project charter outlining responsibilities for each team
D. Conducting a skills assessment to assign team members accordingly

Answer: D

Explanation:
Conducting a skills assessment helps determine which team members are best suited for cognitive AI work, such as model development and data science, and which are better suited for noncognitive automation or traditional implementation tasks. This supports proper team assignment before execution begins.

NEW QUESTION # 317
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?

A. Using AI to automate data tagging
B. Establishing a metadata management system
C. Implementing a continuous data integration process
D. Adopting a data life cycle management tool

Answer: B

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 # 318
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