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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 4

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 5

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 CPMAI_v7 Exam Fee - CPMAI_v7 Knowledge Points

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?

A. Use of Generative Outpainting
B. Prompt engineering for new image generation
C. Use of super#resolution to enhance the existing image
D. Use of inpainting to replace image components

Answer: A

Explanation:
Generative AI encompasses techniques that create new data based on learned patterns. Outpainting is the specific generative approach for extending an image's content beyond its original borders. It leverages the same learned representations as inpainting or super#resolution but focuses on generating entirely new regions around an existing image.

NEW QUESTION # 102
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device," specifically a mobile phone, and the user may sometimes be in remote locations without regular access to the internet.
What's the most important thing to consider here?

A. Make sure the model lives on the edge device so it can be used regardless of internet connection
B. Make sure the model lives in a hybrid environment
C. Make sure that you can use Generative AI solutions on an edge device
D. Make sure the model is available over a cloud-based API

Answer: A

Explanation:
In the Model Operationalization phase (Phase VI), when targeting edge devices, teams must capture the specific constraints around connectivity and on-device performance. The CPMAI Workbook's Edge Model Data Needs task group instructs project teams to list all requirements and constraints for running models on edge systems-including constraints on external data access-and to determine how models will be deployed locally so they function offline when connectivity is unavailable.
Under "Edge constraints," the Workbook requires teams to "List constraints on model usage including ...
requirements for model result response time, constraints on external data access," underscoring that reliance on a remote API is infeasible without connectivity.
Consequently, the critical operationalization decision is to deploy the model directly on the edge device, ensuring it remains fully functional regardless of internet availability.
=========

NEW QUESTION # 103
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?

A. Supervised Learning
B. All would work equally well
C. Reinforcement Learning
D. Unsupervised Learning

Answer: D

Explanation:
When the goal is to uncover hidden structures or groupings in unlabeled data, unsupervised learning-notably clustering algorithms-is the appropriate choice. CPMAI describes clustering as "an unsupervised process that partitions data into groups based on similarity" and calls for applying these methods to discover patterns in unlabeled datasets .
=========

NEW QUESTION # 104
Which of the following best describes the technical definition of Machine Learning?

A. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
B. The use of computing technology to enable machines to gain cognitive intelligence.
C. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
D. The application of pre-defined rules and algorithms to solve complex problems.

Answer: A

Explanation:
Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
=========

NEW QUESTION # 105
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

A. Pick an ensemble method since you're not sure which algorithm will perform best
B. Pick a simple algorithm such as Gaussian mixture
C. Pick a simple algorithm such as naive bayes
D. Pick a neural network algorithm since you know this works well for supervised learning approaches

Answer: C

Explanation:
Naive Bayes classifiers are a family of "simple probabilistic classifiers based on Bayes' theorem with the
'naive' assumption of feature independence," making them fast to train and easy to interpret-ideal for teams new to AI tackling binary tasks like spam detection .
=========

NEW QUESTION # 106
......

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