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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

Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.

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

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 (Q78-Q83):

NEW QUESTION # 78
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. Keep going as planned and the problem will eventually correct itself
D. Try to get additional information from project lead to see how many examples per dimension are needed

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 # 79
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?

A. Responsible AI
B. Explainable AI
C. Governed AI
D. Transparent AI

Answer: A

Explanation:
Under Domain VI: Trustworthy AI in the CPMAI Exam Content Outline, Responsible AI covers establishing policies, guidelines, and governance that ensure AI solutions are developed for positive, ethical use and prevent misuse. Defining proper-use guidelines and embedding ethical intent into facial recognition directly align with Responsible AI practices .

NEW QUESTION # 80
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy.
What is your next best course of action to address this?

A. Share prior experiences with how your last team addressed this problem and their data quality issues
B. Say nothing and let the team figure it out for themselves
C. Help senior management do research on AI vendors
D. Share prior experiences with how your last team addressed this problem and how you solved it

Answer: D

Explanation:
CPMAI's Differentiate AI Project Management Approaches task stresses that effective AI adoption requires building internal capabilities and understanding domain-specific challenges. By sharing your own team's past experiences-how you diagnosed the problem, structured the data, and developed AI solutions-you guide leadership toward establishing a homegrown, iterative AI practice rather than simply purchasing a black-box product .

NEW QUESTION # 81
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 adjust the scope of this interaction to continue with the project
C. See if you can expand the scope to continue with the project
D. See if you already have access to enough data to continue with the project

Answer: C

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 # 82
Clean, well-labeled datasets used for machine learning are partitioned into three subsets: Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Use the same data for all sets
B. Split by random subsampling
C. Split by alphabetical order
D. Split by patterned subsampling

Answer: B

Explanation:
CPMAI's glossary defines data splitting as "dividing a data set into subsets (e.g., training, validation, test) for model development and evaluation," typically achieved via random subsampling to ensure each subset is representative of the underlying distribution and to prevent sampling bias.

NEW QUESTION # 83
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