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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

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

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 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 (Q72-Q77):

NEW QUESTION # 72
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Select all that apply.)

A. Programmatic automated content generation
B. Virtual Avatars and Characters
C. Explainable Decision-support systems
D. Data Augmentation for Training
E. Content Generation
F. Human Augmentation

Answer: A,B,D,E,F

Explanation:
The CPMAI Glossary's entry for Generative AI highlights its use in creating new content (text, images, or code), enhancing training datasets via data augmentation, powering virtual avatars/characters, and serving as an Augmented Intelligence tool to boost human productivity . It also underpins programmatic content generation across multiple media types. Generative AI is not designed primarily for explainable decision- support interfaces.
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NEW QUESTION # 73
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?

A. Perform Data Transformation & Multiplication
B. Purchase the data from a third party
C. Perform Data Sampling
D. Perform Data Anonymization

Answer: A

Explanation:
In CPMAI v7's Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities-including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This
"augmentation" is described as "constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes" .
Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine "What transformation or multiplication activities can be done to increase training data volume while maintaining quality" . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.
=========

NEW QUESTION # 74
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

A. Invest in technology solutions to solve the problem
B. Have the right culture for AI
C. Start with basic automation and introduce AI only as needed
D. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes

Answer: B

Explanation:
CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I's Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI-ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .
=========

NEW QUESTION # 75
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

A. Prompt engineering skills
B. Strong math skills, especially in calculus and statistics
C. Critical thinking skills
D. Understanding of algorithms
E. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
F. Automation skills, especially around creating RPA bots

Answer: B,C,D,E

Explanation:
In Phase I's AI Skills Assessment, CPMAI directs teams to "List the cognitive skills you have available" and to identify "What expertise and skills you have available to you that you can use for this project" as well as any skills gaps to address . The methodology-and the CPMAI Glossary's definition of a data scientist- emphasizes core competencies in:
Data Engineering & Preparation (manipulating, collecting, transforming large data sets) Critical Thinking (interpreting insights to align with business goals) Algorithmic Understanding (selecting and applying the right statistical or ML models) Mathematical Proficiency (especially statistics and calculus underpinning model creation) By contrast, prompt engineering (A) is a specialized role for LLM interactions, not a general data-science core competency; and RPA-centric automation skills (E) fall outside the CPMAI focus on cognitive/ML capabilities.
=========

NEW QUESTION # 76
You're creating an AI-enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Select all that apply.)

A. Data Quality Issues
B. Data Quantity Issues
C. Change Management Issues
D. Privacy Risks
E. Data Sharing challenges
F. Business Risks
G. Security Risks

Answer: A,D,E,G

Explanation:
Domain IV: Data for AI - Task 2: Implementing Data Governance and Management mandates establishing data stewardship, management plans, lineage, and master-data practices. Core governance concerns include how data is shared (A), ensuring user privacy (B), guarding against breaches (E), and maintaining high data quality (F). Quantity or change-management issues are operational rather than governance controls.
=========

NEW QUESTION # 77
......

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