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PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?

A. Phase I
B. Phase V
C. Phase IV
D. Phase III
E. Phase II
F. Phase VI

Answer: C

Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.

NEW QUESTION # 43
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project-AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?

A. Determine if the project fits within the scope, budget, and timeline set out.
B. Determine whether the project has a cognitive technology component and meets a short-term need.
C. Determine the long-term need for the organization and build the project to that long-term goal.
D. Evaluate whether the solution can be done with automation.

Answer: D

Explanation:
During Phase I: Business Understanding, one of the foundational CPMAI tasks is to "determine when to implement automation versus AI," ensuring that rule-based or non-cognitive alternatives are considered first and AI is only selected when those approaches won't suffice.
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NEW QUESTION # 44
Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?

A. They did not practice model versioning and keep all versions of the model
B. They did not have a model retraining pipeline that took into account models
C. They did not have data governance in place
D. They did not practice model iteration and properly iterate on the model

Answer: A

Explanation:
In Phase VI: Model Operationalization of the CPMAI v7 methodology, project teams must explicitly plan for "model versioning and iteration" as part of deploying and maintaining models in production. Overwriting the original model without preserving its prior version prevents rollback and comparison, which is a core requirement for robust AI operations.
The Workbook states that operationalization considerations include "model versioning and iteration" to ensure that previous model artifacts are retained and that updates can be managed safely.
Additionally, under Edge Model Data Needs, teams are instructed to "Determine methods for model versioning and update" to support proper tracking and governance of model changes across iterations.

NEW QUESTION # 45
During which phase of an AI project should you consider Trustworthy AI considerations?

A. Phase VI: Model Operationalization
B. Phase II: Data Understanding
C. Phase I: Business Understanding
D. Every Phase of the AI project

Answer: D

Explanation:
CPMAI's Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase-from Business Understanding through Operationalization-so that trustworthy practices are not an afterthought but a continuous, integrated activity .
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NEW QUESTION # 46
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

A. Data Mining or Data Analytics
B. Data Integration
C. Data Governance
D. Data preparation

Answer: A

Explanation:
CPMAI defines analytics as "the use of statistical and computational methods to extract meaningful insights from data." When the goal is to discover and highlight patterns and insights within existing datasets, applying data mining or analytics techniques is the appropriate action.

NEW QUESTION # 47
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