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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

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 3

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 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.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

A. RPA is not the right solution to this problem
B. Unattended bot
C. Business Process Outsourcing
D. Attended bot

Answer: B

Explanation:
In CPMAI's examination of AI patterns, Unattended bots are designed to run autonomously in back-office environments without human supervision, executing repetitive tasks like data entry at scale. This contrasts with Attended bots, which require a user to trigger or interact with them in real time.
Thought for 13 seconds

NEW QUESTION # 26
Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through "trial and error." Which type of machine learning approach is being leveraged for this exercise?

A. Unsupervised Learning
B. Reinforcement Learning
C. Reinforcement Learning is defined in CPMAI as the paradigm where agents learn optimal actions via interactions labeled by reward/punishment signals-essentially a "trial and error" process. Domain III of the CPMAI Exam Content Outline covers "Design reinforcement learning approaches with appropriate agents and environments," confirming that simulation-based, trial-and-error optimization is the hallmark of Reinforcement Learning .
D. All would work equally well
E. Supervised Learning

Answer: C

NEW QUESTION # 27
Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

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

Answer: B

Explanation:
Reinforcement Learning (RL) is specifically designed for sequential decision-making tasks where an agent interacts with an environment and learns optimal behaviors through trial-and-error and reward signals.
Optimizing robot paths on a warehouse floor-deciding which movements lead to higher throughput-is a classic RL use case.
=========

NEW QUESTION # 28
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. Perform Data Anonymization
C. Perform Data Sampling
D. Purchase the data from a third party

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 # 29
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
B. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
C. Collect enormous amounts of data - the more data the better.
D. Understanding which algorithms are best for your data needs.

Answer: A

Explanation:
CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.

NEW QUESTION # 30
......

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