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2025 CPMAI_v7: Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam–High-quality Real Dumps

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

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

Data for AI: This domain targets the Data
AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.

Topic 3

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 4

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 (Q87-Q92):

NEW QUESTION # 87
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?

A. You have selected the wrong data
B. You are underfitting the data
C. You are overfitting the data
D. You have selected the wrong algorithm

Answer: C

Explanation:
Overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, causing it to perform well on seen data but poorly on unseen data. The CPMAI Glossary defines overfitting as "a modeling error where a model learns the training data too well, including its noise, resulting in poor performance on new data."
=========

NEW QUESTION # 88
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

A. Provide a means to reverse-engineer the algorithm to inspect its performance
B. Provide a means to interpret AI results so that cause and effect can be represented.
C. Provide a means to have a different team on the project
D. Provide a means to have contestability of the algorithm selected

Answer: B

Explanation:
Under Required AI Explainability Considerations, CPMAI mandates that when a chosen model is a "black- box" with limited native interpretability, teams must implement post-hoc interpretability techniques (e.g., feature#importance plots, surrogate models) to "interpret AI results so that cause and effect can be represented," ensuring stakeholders understand why the model makes its predictions.
=========

NEW QUESTION # 89
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Store the data in a data warehouse
B. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C. Use noise reduction techniques to reduce all forms of data noise
D. Implement a new data privacy policy

Answer: B

Explanation:
Under CPMAI Phase III: Data Preparation, the Data Format task includes "Data anonymization" as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility.
=========

NEW QUESTION # 90
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. Model operations
B. Data operations
C. Model discovery
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.
=========

NEW QUESTION # 91
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

A. See if you can purchase the data needed to continue with the project
B. See if you can expand the scope to continue with the project
C. See if you can adjust the scope of this interaction to continue with the project
D. See if you already have access to enough data to continue with the project

Answer: B

Explanation:
In Phase II: Data Understanding, CPMAI urges teams to rigorously assess data feasibility-asking whether the data is available, sufficient in quality, and properly aligned with business goals-and to perform a Go/No- Go decision before proceeding . Expanding project scope in the face of data issues violates the methodology's iterative, scope-controlled approach. Instead, CPMAI recommends either down-scoping (Option C), verifying existing data sufficiency (Option B), or identifying necessary data sources (Option D) to resolve issues without amplifying risk.

NEW QUESTION # 92
......

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