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PMI CPMAI_v7 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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 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 Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?

Answer: B

Explanation:
The Phase IV: Model Development generic tasks include:
Select Modeling Technique (algorithm selection)
Generate model test design
Model Training / Model Building
Hyperparameter Optimization (model tuning)
Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .
=========


NEW QUESTION # 74
An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

Answer: D

Explanation:
Training deep neural networks on CPUs is very slow. CPMAI's Glossary highlights that tensor processing units (TPUs) and GPUs are specialized hardware accelerators explicitly recommended to "accelerate the training and inference of machine learning models" by parallelizing the heavy matrix operations in neural- network layers. Switching from desktop CPU training to GPU-based training can reduce training time by orders of magnitude.
=========


NEW QUESTION # 75
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?

Answer: C

Explanation:
The Transfer Learning task in Phase IV: Model Development recommends leveraging a pre-trained model as the starting point for a new, related task. By fine-tuning rather than training from scratch, teams dramatically reduce compute time and data requirements-ideal for tight iteration cycles.


NEW QUESTION # 76
Your team is testing the NLP model they just created to make sure it's performing as expected. Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?

Answer: A

Explanation:
Phase V of the CPMAI v7 methodology-Model Evaluation and Maintenance-includes a formal CPMAI Phase V Go/No-Go assessment before any model can be moved into production or on to the next iteration.
This checkpoint ensures that the model meets predefined business success criteria, quality metrics, and risk considerations prior to deployment. Skipping this Go/No-Go review bypasses critical governance questions and undermines the integrity of the AI lifecycle.
=========


NEW QUESTION # 77
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?

Answer: B

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 # 78
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