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PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?

A. Find a project that requires 100% accuracy in the results and start with that one.
B. Go through all possible scenarios to come up with the perfect first project.
C. Ask key stakeholders from your group and find a small problem that would have a big return on investment and start there.
D. Ask key stakeholders from all groups for input about their pain points.

Answer: C

Explanation:
In Phase I: Business Understanding, CPMAI directs teams to "determine business objectives" by engaging stakeholders to surface specific pain points, estimate time-to-ROI, and prioritize projects that deliver tangible business value quickly. Focusing on a narrowly scoped problem with high ROI ensures early success, builds momentum, and validates the AI methodology before tackling larger or more complex initiatives.
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NEW QUESTION # 97
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 neural network algorithm since you know this works well for supervised learning approaches
C. Pick a simple algorithm such as Gaussian mixture
D. Pick a simple algorithm such as naive bayes

Answer: D

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 .
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NEW QUESTION # 98
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?

A. All roles are critical to staff in the Four different AI Tech environments
B. Data Scientists
C. Data Engineering and Data Scientists
D. Data Engineering
E. Senior management

Answer: D

Explanation:
CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams "create a reusable data pipeline to collect, ingest, and prepare data for training" and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists.
While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.

NEW QUESTION # 99
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?

A. Data selection
B. Dimensionality Reduction
C. Multivariate regression
D. Gradient Descent

Answer: B

Explanation:
The process of reducing a dataset's feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t#distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.

NEW QUESTION # 100
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

A. Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
B. Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
C. Develop a unique profile of each individual, and have that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
D. Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user

Answer: B

Explanation:
The Hyperpersonalization pattern is defined as tailoring experiences based on individual user characteristics or behavior-requiring each profile to learn and adapt continuously as more data arrives. Manually updating or pre-programming profiles undermines this dynamic learning capability.
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NEW QUESTION # 101
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