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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

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 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.

Topic 4

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 5

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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?

A. Biased data sets leading to algorithmic discrimination
B. Data privacy issues leading to data sharing concerns
C. Nothing is wrong, algorithmic decisions will never be 100%
D. Generative AI models hallucinating data results

Answer: A

Explanation:
When training data under-represents or skews certain groups, the resulting model can systematically discriminate against those groups-a phenomenon termed algorithmic discrimination in the CPMAI Glossary.
Such bias in outcomes arises directly from biased training data.
Moreover, the CPMAI Exam Content Outline emphasizes that Trustworthy AI must apply laws pertaining to AI ethics, bias, and fairness to detect and remediate these issues early in the lifecycle.
=========

NEW QUESTION # 45
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data-centric projects.
What's the next best course of action?

A. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
B. Bring in data centric methodology best practices to get this project back on track
C. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
D. Hire an entirely new team making sure there is at least one data scientist on this new team

Answer: B

Explanation:
Domain II of the CPMAI Exam Content Outline highlights the need to "adapt traditional methodologies for data-centric projects" and "implement continuous AI project lifecycles" rather than treating AI as conventional software development. Bringing in CPMAI's data-centric best practices-phased, iterative, and focused on data understanding/preparation-directly addresses the root causes of AI project failures and realigns the team to proven AI project management frameworks.

NEW QUESTION # 46
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A. Applying a hybrid approach of automation and AI best practices would have achieved better results.
B. Nothing - running automation projects like autonomous projects is the correct thing to do.
C. Senior management should become involved in the project.
D. Everyone on the team should understand the differences between automation and autonomous systems.

Answer: D

Explanation:
During Phase I's Cognitive Project Requirements tasks, CPMAI instructs teams to "Determine when to implement automation versus AI." Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.
=========

NEW QUESTION # 47
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

A. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
B. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
C. Move forward without determining which pattern of AI this falls under.
D. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.

Answer: B

Explanation:
In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification-mapping business objectives to one or more of the Seven Patterns of AI-so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .

NEW QUESTION # 48
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?

A. Model training
B. Model tuning
C. Model Selection
D. Algorithm Selection

Answer: C

Explanation:
The Phase IV: Model Development generic tasks include:
Select Modeling Technique (algorithm selection)
Generate model test design
Model Training / Model Building
Hyperparameter Optimization (model tuning)
Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .
=========

NEW QUESTION # 49
......

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