QUIZ 2025 PMI CPMAI_V7 PASS-SURE VALID PRACTICE QUESTIONS

Quiz 2025 PMI CPMAI_v7 Pass-Sure Valid Practice Questions

Quiz 2025 PMI CPMAI_v7 Pass-Sure Valid Practice Questions

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Tags: Valid CPMAI_v7 Practice Questions, Free CPMAI_v7 Study Material, Vce CPMAI_v7 Test Simulator, Reliable CPMAI_v7 Exam Questions, Latest CPMAI_v7 Test Question

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

  • A. Data Integration
  • B. Data preparation
  • C. Data Mining or Data Analytics
  • D. Data Governance

Answer: C

Explanation:
CPMAI defines analytics as "the use of statistical and computational methods to extract meaningful insights from data." When the goal is to discover and highlight patterns and insights within existing datasets, applying data mining or analytics techniques is the appropriate action.


NEW QUESTION # 90
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?

  • A. Phase I
  • B. Phase II
  • C. Phase VI
  • D. Phase IV
  • E. Phase V
  • F. Phase III

Answer: F

Explanation:
Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers "identifying methods for data labeling and engaging in data labeling efforts," which is essential for supervised learning workflows like image recognition.
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NEW QUESTION # 91
You're creating an AI-enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Select all that apply.)

  • A. Data Sharing challenges
  • B. Privacy Risks
  • C. Change Management Issues
  • D. Business Risks
  • E. Data Quantity Issues
  • F. Security Risks
  • G. Data Quality Issues

Answer: A,B,F,G

Explanation:
Domain IV: Data for AI - Task 2: Implementing Data Governance and Management mandates establishing data stewardship, management plans, lineage, and master-data practices. Core governance concerns include how data is shared (A), ensuring user privacy (B), guarding against breaches (E), and maintaining high data quality (F). Quantity or change-management issues are operational rather than governance controls.
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NEW QUESTION # 92
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

  • A. Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
  • B. Develop a unique profile of each individual, and have that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
  • C. Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
  • D. Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes

Answer: C

Explanation:
The Hyperpersonalization pattern is defined as tailoring experiences based on individual user characteristics or behavior-requiring each profile to learn and adapt continuously as more data arrives. Manually updating or pre-programming profiles undermines this dynamic learning capability.
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NEW QUESTION # 93
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?

  • A. Data Engineering and Data Scientists
  • B. Data Engineering
  • C. All roles are critical to staff in the Four different AI Tech environments
  • D. Data Scientists
  • E. Senior management

Answer: B

Explanation:
CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams "create a reusable data pipeline to collect, ingest, and prepare data for training" and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists.
While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.


NEW QUESTION # 94
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