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100% Pass 2025 PMI CPMAI_v7: Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam–High-quality Sample Exam

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.

Topic 2

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 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 (Q58-Q63):

NEW QUESTION # 58
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 "Goldilocks" approach
B. The "more is better" approach
C. There is no correct approach
D. The "less is better" approach

Answer: A

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 # 59
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?

A. Data selection
B. Multivariate regression
C. Dimensionality Reduction
D. Gradient Descent

Answer: C

Explanation:
The process of reducing a dataset's feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t#distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.

NEW QUESTION # 60
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 get additional data - at least 5 training examples for each dimension in the representation
B. Try to improve the quality of your data through more preparation
C. Try to get additional information from project lead to see how many examples per dimension are needed
D. Keep going as planned and the problem will eventually correct itself

Answer: A

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 # 61
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?

A. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're likely to expand to in the coming year
B. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
C. You're too small of an organization to be worried about laws at the moment
D. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States

Answer: A

Explanation:
CPMAI's Trustworthy AI - Navigating AI Regulations and Frameworks tasks require continuously monitoring "AI-relevant data privacy laws and regulations" both where you operate today and where you plan to expand . Furthermore, the Workbook's Task: Required Compliance with Regulations and Laws instructs teams to identify all laws and regulations that might apply based on location, industry, and other factors, ensuring legal and liability risks are addressed before operationalization .
=========

NEW QUESTION # 62
You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?

A. Autonomous patterns and process automation
B. Decision tree and Random Forest approaches
C. Image and object detection and recognition systems
D. Generative AI solutions for content generation

Answer: D

Explanation:
Generative AI is defined in the CPMAI Glossary as "AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data." Using Generative AI for content generation directly addresses the need to produce marketing images automatically.
=========

NEW QUESTION # 63
......

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