NEW CPMAI_V7 TEST TIPS & CPMAI_V7 EXAM COST

New CPMAI_v7 Test Tips & CPMAI_v7 Exam Cost

New CPMAI_v7 Test Tips & CPMAI_v7 Exam Cost

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?

  • A. Data operations
  • B. Model discovery
  • C. Model operations
  • D. Model Governance

Answer: D

Explanation:
In Phase VI: Model Operationalization, CPMAI specifies that a Model Governance Framework must be established before any model versioning or retraining occurs. This governance framework ensures proper version control, audit trails, and clear ownership for each model iteration, maintaining accountability and compliance throughout the model lifecycle.
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NEW QUESTION # 97
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?

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

Answer: C

Explanation:
Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .


NEW QUESTION # 98
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

  • A. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
  • B. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
  • C. Make sure all senior leadership is data literate, understands the V's of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
  • D. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

Answer: C

Explanation:
CPMAI's Domain IV: Data for AI - Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders-not just technical practitioners-must grasp the core characteristics of Big Data (the V's: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.
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NEW QUESTION # 99
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?

  • A. Predictive Analytics & Decision Support and Patterns and Anomalies
  • B. Goal Driven Systems
  • C. Recognition Pattern
  • D. Predictive Analytics & Decision Support

Answer: D

Explanation:
The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.
A CPMAI Glossary self-test question states that Predictive Analytics "uses historical data to forecast future outcomes" .
Another glossary question defines predictive analytics as aiming "to use historical data to forecast future outcomes" .
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NEW QUESTION # 100
Clean, well-labeled datasets used for machine learning are partitioned into three subsets: Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

  • A. Use the same data for all sets
  • B. Split by alphabetical order
  • C. Split by patterned subsampling
  • D. Split by random subsampling

Answer: D

Explanation:
CPMAI's glossary defines data splitting as "dividing a data set into subsets (e.g., training, validation, test) for model development and evaluation," typically achieved via random subsampling to ensure each subset is representative of the underlying distribution and to prevent sampling bias.


NEW QUESTION # 101
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