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[Hardware] CPMAI_v7 Quiz Practice Materials - CPMAI_v7 Quiz Torrent & CPMAI_v7 Test Boo

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【Hardware】 CPMAI_v7 Quiz Practice Materials - CPMAI_v7 Quiz Torrent & CPMAI_v7 Test Boo

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PMI CPMAI_v7 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.
Topic 3
  • 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 4
  • Data for AI: This domain targets the Data
  • AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q93-Q98):NEW QUESTION # 93
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. Enhance revenue
  • B. Solves an already solved problem but does it better and cheaper
  • C. Solves a previously unsolved problem
  • D. Improve company competitiveness in the market
  • E. Has the "cool" factor
  • F. Explores a proof of concept for an AI project
Answer: A,B,C,D
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 # 94
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?
  • A. When in doubt, don't apply any pattern of AI.
  • B. Apply every pattern to the project.
  • C. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
  • D. When in doubt, go with the Patterns & Anomalies pattern as all AI projects are about pattern matching.
Answer: C
Explanation:
CPMAI's Task: AI Pattern Identification requires teams to map their specific business objectives to the most appropriate one or more of the Seven Patterns of AI. Starting from "what are we trying to accomplish?" and then selecting the pattern(s) that align with those goals is the prescribed approach.
=========

NEW QUESTION # 95
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?
  • A. Responsible AI
  • B. Explainable AI
  • C. Governed AI
  • D. AI Systemic Transparency
  • E. Transparent AI
Answer: E
Explanation:
In CPMAI's Trustworthy AI framework, Transparent AI focuses on providing clear documentation of data sources, modeling approaches, evaluation methods, and deployment plans so that stakeholders can audit and understand how the system was built. The users' request for "visibility into all aspects" of model development, training data, and test sets directly maps to the Required AI Transparency Considerations task early in the methodology .

NEW QUESTION # 96
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?
  • A. Phase IV
  • B. Phase III
  • C. Phase II
  • D. Phase I
  • E. Phase V
  • F. Phase VI
Answer: A
Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.

NEW QUESTION # 97
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data-centric projects.
What's the next best course of action?
  • A. Hire an entirely new team making sure there is at least one data scientist on this new team
  • B. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
  • C. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
  • D. Bring in data centric methodology best practices to get this project back on track
Answer: D
Explanation:
Domain II of the CPMAI Exam Content Outline highlights the need to "adapt traditional methodologies for data-centric projects" and "implement continuous AI project lifecycles" rather than treating AI as conventional software development. Bringing in CPMAI's data-centric best practices-phased, iterative, and focused on data understanding/preparation-directly addresses the root causes of AI project failures and realigns the team to proven AI project management frameworks.

NEW QUESTION # 98
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