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PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?

A. Transparent AI
B. AI Systemic Transparency
C. Explainable AI
D. Governed AI
E. Responsible AI

Answer: A

Explanation:
In CPMAI's Trustworthy AI framework, Transparent AI focuses on providing clear documentation of data sources, modeling approaches, evaluation methods, and deployment plans so that stakeholders can audit and understand how the system was built. The users' request for "visibility into all aspects" of model development, training data, and test sets directly maps to the Required AI Transparency Considerations task early in the methodology .

NEW QUESTION # 71
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?

A. Transfer Learning
B. Generative AI
C. Reinforcement Learning
D. Pre-Trained Models

Answer: A

Explanation:
Transfer learning is defined as a technique that "leverages a pretrained model (trained on a large, relevant data set) as a starting point for a new, related task," allowing you to reuse learned representations (e.g., convolutional layers trained on one image domain) when training on another domain (like cars # animals) .
=========

NEW QUESTION # 72
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. Multivariate regression
C. Dimensionality Reduction
D. Gradient Descent

Answer: C

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 # 73
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?

A. Predictive Analytics & Decision Support
B. Goal Driven Systems
C. Recognition Pattern
D. Predictive Analytics & Decision Support and Patterns and Anomalies

Answer: A

Explanation:
The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.
A CPMAI Glossary self-test question states that Predictive Analytics "uses historical data to forecast future outcomes" .
Another glossary question defines predictive analytics as aiming "to use historical data to forecast future outcomes" .
=========

NEW QUESTION # 74
Major factors for the project you are currently working on are around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

A. Gaussian Mixture
B. Neural Networks
C. Support Vector Machines (SVM)
D. Naive Bayes

Answer: B

Explanation:
Neural Networks-especially deep architectures-typically require extensive computational resources, longer training times, and higher infrastructure costs compared to simpler methods. In contrast, algorithms like Naive Bayes train very quickly on large datasets, and Gaussian Mixture Models or SVMs have more moderate training complexity and infrastructure demands. Therefore, given strict constraints on training time, cost, and complexity, Neural Networks are the least suitable choice.
=========

NEW QUESTION # 75
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