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[General] CPMAI_v7 Pdf Format & New CPMAI_v7 Test Online

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【General】 CPMAI_v7 Pdf Format & New CPMAI_v7 Test Online

Posted at 5 day before      View：15 | Replies：2        Print      Only Author   [Copy Link] 1#
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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
  • Machine Learning: This section is aimed at the Data
  • AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.
Topic 3
  • Domain VI Trustworthy AI: This section is designed for the Project Manager and focuses on ethical, responsible, and transparent AI development. It covers building trustworthy systems, dispelling misconceptions, evaluating real-world ethical concerns, defining responsible frameworks, and implementing mitigation tactics for unintended harms. It addresses data privacy, GDPR compliance, protection of PII, anonymization techniques, security against adversarial threats, and monitoring.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q19-Q24):NEW QUESTION # 19
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. Pre-Trained Models
  • B. Generative AI
  • C. Transfer Learning
  • D. Reinforcement Learning
Answer: C
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 # 20
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
  • A. Governed AI
  • B. Explainable AI
  • C. Responsible AI
  • D. Ethical AI
  • E. Transparent AI
Answer: B
Explanation:
In CPMAI's Trustworthy AI requirements, the Explainable AI layer specifically covers "legal, compliance, and risk considerations [that] might require that the AI system used for decision-making ... provide some level of explainability for audit, root cause analysis, or other purposes." Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result-exactly the focus of the Required AI Explainability Considerations task.
=========

NEW QUESTION # 21
Enhancing and cleaning data is an important action during which phase of CPMAI?
  • A. Phase II
  • B. Phase VI
  • C. Phase I
  • D. Phase III
  • E. Phase V
  • F. Phase IV
Answer: D
Explanation:
Phase III: Data Preparation is dedicated to all data-centric tasks, including cleaning ("Clean data") and enhancement ("Enhance & Augment data") of raw inputs so they're ready for model consumption.
=========

NEW QUESTION # 22
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?
  • A. Use brute force method
  • B. Use cloud-based technology
  • C. Use data from a previous project
  • D. Use or extend a pre-trained model
Answer: D
Explanation:
The Transfer Learning task in Phase IV: Model Development recommends leveraging a pre-trained model as the starting point for a new, related task. By fine-tuning rather than training from scratch, teams dramatically reduce compute time and data requirements-ideal for tight iteration cycles.

NEW QUESTION # 23
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?
  • A. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
  • B. Collect enormous amounts of data - the more data the better.
  • C. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
  • D. Understanding which algorithms are best for your data needs.
Answer: C
Explanation:
CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.

NEW QUESTION # 24
......
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Posted at yesterday 15:45        Only Author  2#
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Posted at 4 hour before        Only Author  3#
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