100% PASS-RATE PMI NEW CPMAI_V7 DUMPS FREE & AUTHORIZED DUMPSFREE - LEADING OFFER IN QUALIFICATION EXAMS

100% Pass-Rate PMI New CPMAI_v7 Dumps Free & Authorized DumpsFree - Leading Offer in Qualification Exams

100% Pass-Rate PMI New CPMAI_v7 Dumps Free & Authorized DumpsFree - Leading Offer in Qualification Exams

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What' s your best course of action?

  • A. Try to improve the quality of your data through more preparation
  • B. Keep going as planned and the problem will eventually correct itself
  • C. Try to get additional information from project lead to see how many examples per dimension are needed
  • D. Try to get additional data - at least 5 training examples for each dimension in the representation

Answer: D

Explanation:
CPMAI's Phase II: Data Understanding includes verifying that you have sufficient data volume for each feature to support reliable model training. The learning curve concept underscores that model performance improves with additional training examples. When dimensions are under-represented, the team must source or generate more data-aiming for a minimum number of examples per feature-to avoid underfitting and ensure stable predictions.


NEW QUESTION # 70
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 the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
  • B. 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.
  • 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 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.

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 # 71
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?

  • A. Biased data sets leading to algorithmic discrimination
  • B. Data privacy issues leading to data sharing concerns
  • C. Generative AI models hallucinating data results
  • D. Nothing is wrong, algorithmic decisions will never be 100%

Answer: A

Explanation:
When training data under-represents or skews certain groups, the resulting model can systematically discriminate against those groups-a phenomenon termed algorithmic discrimination in the CPMAI Glossary.
Such bias in outcomes arises directly from biased training data.
Moreover, the CPMAI Exam Content Outline emphasizes that Trustworthy AI must apply laws pertaining to AI ethics, bias, and fairness to detect and remediate these issues early in the lifecycle.
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NEW QUESTION # 72
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?

  • A. Data Drift
  • B. Model Drift
  • C. An incomplete milestone list
  • D. Lack of stakeholder support

Answer: A

Explanation:
A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
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NEW QUESTION # 73
Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through "trial and error." Which type of machine learning approach is being leveraged for this exercise?

  • A. Reinforcement Learning is defined in CPMAI as the paradigm where agents learn optimal actions via interactions labeled by reward/punishment signals-essentially a "trial and error" process. Domain III of the CPMAI Exam Content Outline covers "Design reinforcement learning approaches with appropriate agents and environments," confirming that simulation-based, trial-and-error optimization is the hallmark of Reinforcement Learning .
  • B. Unsupervised Learning
  • C. Supervised Learning
  • D. All would work equally well
  • E. Reinforcement Learning

Answer: A


NEW QUESTION # 74
......

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