Quiz 2025 Perfect PMI CPMAI_v7: Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Complete Exam Dumps

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.
Topic 2	AI Fundamentals: This section measures the abilities of a Project Manager and explores foundational AI concepts, including its definition, links to human cognition, and differences across AGI, Strong, Weak, and Narrow AI. It includes understanding the Turing Test and cognitive computing, dispelling myths, and applying augmented intelligence in business contexts. The historical progression of AI, such as AI winters, symbolic logic, expert systems, and fuzzy logic, is examined along with reasons for AI's current prominence and its role in digital transformation. The section continues to assess the identification of suitable AI use cases, understanding limitations, and adoption patterns like conversational AI, speech processing, anomaly detection, RPA, goal-driven systems, and integrated AI solutions.
Topic 3	Machine Learning: This section is aimed at the Data AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
You're being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you're actually solving a real business problem.
During Phase I: Business Understanding, you should consider at least one of the following (Select all that apply):

A. Improve company competitiveness in the market
B. Has the "cool" factor
C. Solves a previously unsolved problem
D. Enhance revenue
E. Explores a proof of concept for an AI project
F. Solves an already solved problem but does it better and cheaper

Answer: A,C,D,F

Explanation:
Phase I begins with Determine Business Objectives to ensure the AI initiative aligns to real business needs.
Teams evaluate whether the solution will enhance revenue, solve a previously unsolved problem, improve competitive positioning, or "provide enough ROI/impact...and be better/more impactful than the current, non- cognitive, heuristic approach" (i.e., do something already addressed but do it better and cheaper). Proof-of- concepts (A) and "cool" factors (E) are not valid business objectives under CPMAI's Business Understanding tasks.

NEW QUESTION # 67
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind. At what phase of CPMAI would Trustworthy AI be considered?

A. Phase V
B. Phase IV
C. Phase I
D. None of the phases
E. Phase III
F. Phase VI
G. All phases
H. Phase II

Answer: G

Explanation:
Trustworthy AI is not confined to a single phase but is woven throughout the entire CPMAI lifecycle:
The CPMAI Exam Content Outline under Domain VI: Trustworthy AI specifies tasks such as "Apply ethical AI concepts throughout the development lifecycle," "Ensure compliance with privacy/security requirements," and "Implement transparency and explainability" at every stage .
The CPMAI Workbook's Task Group: Trustworthy AI Requirements (covering transparency, explainability, ethics, compliance, and responsible-AI frameworks) appears as an overarching set of artifacts and considerations that map back to multiple phases-beginning with Business Understanding and continuing through Model Operationalization .
Thus, Trustworthy AI considerations apply across all CPMAI phases.

NEW QUESTION # 68
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?

A. Move forward with the project as planned. The problem definition will become clear later on in the project.
B. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go
/No Go decision chart to start your project.
C. Do not move forward and cancel the project altogether.
D. Do not move forward until you can determine a clear problem definition.

Answer: D

Explanation:
In Phase I's AI Go/No Go task group, the Business Feasibility step mandates that every business-feasibility question-including a clear problem definition-must be answered "Go" before proceeding. If any critical feasibility criteria remain unanswered or "No Go," the project must pause and resolve those uncertainties rather than advance prematurely.
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NEW QUESTION # 69
Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. All would work equally well

Answer: C

Explanation:
Reinforcement Learning (RL) is specifically designed for sequential decision-making tasks where an agent interacts with an environment and learns optimal behaviors through trial-and-error and reward signals.
Optimizing robot paths on a warehouse floor-deciding which movements lead to higher throughput-is a classic RL use case.
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NEW QUESTION # 70
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

A. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes
B. Invest in technology solutions to solve the problem
C. Have the right culture for AI
D. Start with basic automation and introduce AI only as needed

Answer: C

Explanation:
CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I's Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI-ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .
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NEW QUESTION # 71
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