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[General] New PMI CPMAI_v7 Exam Name & CPMAI_v7 New Exam Bootcamp

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【General】 New PMI CPMAI_v7 Exam Name & CPMAI_v7 New Exam Bootcamp

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q46-Q51):NEW QUESTION # 46
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?
  • A. Transfer Learning
  • B. Reinforcement Learning
  • C. Generative AI
  • D. Pre-Trained Models
Answer: A
Explanation:
Transfer learning is defined as a technique that "leverages a pretrained model (trained on a large, relevant data set) as a starting point for a new, related task," allowing you to reuse learned representations (e.g., convolutional layers trained on one image domain) when training on another domain (like cars # animals) .
=========

NEW QUESTION # 47
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?
  • A. Supervised Learning
  • B. Unsupervised Learning
  • C. All would work equally well
  • D. Reinforcement Learning
Answer: B
Explanation:
When the goal is to uncover hidden structures or groupings in unlabeled data, unsupervised learning-notably clustering algorithms-is the appropriate choice. CPMAI describes clustering as "an unsupervised process that partitions data into groups based on similarity" and calls for applying these methods to discover patterns in unlabeled datasets .
=========

NEW QUESTION # 48
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. 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.
  • B. Do not move forward until you can determine a clear problem definition.
  • C. Do not move forward and cancel the project altogether.
  • D. Move forward with the project as planned. The problem definition will become clear later on in the project.
Answer: B
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.
=========

NEW QUESTION # 49
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)
  • A. The algorithm code
  • B. Hyperparameter settings
  • C. Bias-variance tradeoff
  • D. Supporting training data
Answer: A,B,D
Explanation:
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
=========

NEW QUESTION # 50
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?
  • A. The "less is better" approach
  • B. The "Goldilocks" approach
  • C. The "more is better" approach
  • D. There is no correct approach
Answer: B
Explanation:
Phase II: Data Understanding centers on identifying just the right amount of data for model training-neither too little (risking underfitting) nor too much (wasting resources and introducing noise). This balanced-
"Goldilocks"-approach ensures you collect sufficient high-quality, relevant records to meet cognitive objectives without incurring unnecessary cost or complexity.
=========

NEW QUESTION # 51
......
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