CPMAI_v7 Exam Dumps - Cognitive Project Management in AI CPMAI v7 - Training & Certification

The CPMAI Glossary defines a naive Bayes classifier as “a family of simple probabilistic classifiers based on Bayes’ theorem with the assumption of feature independence,” making it ideal for small or limited datasets where model simplicity and interpretability are priorities.

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Phase I begins with Determine Business Objectives to ensure the AI initiative aligns to real business needs. Teams evaluate whether the solution will enhance revenue, solve a previously unsolved problem, improve competitive positioning, or “provide enough ROI/impact…and be better/more impactful than the current, non-cognitive, heuristic approach” (i.e., do something already addressed but do it better and cheaper). Proof-of-concepts (A) and “cool” factors (E) are not valid business objectives under CPMAI’s Business Understanding tasks.

CPMAI’s Differentiate AI Project Management Approaches task stresses that effective AI adoption requires building internal capabilities and understanding domain-specific challenges. By sharing your own team’s past experiences—how you diagnosed the problem, structured the data, and developed AI solutions—you guide leadership toward establishing a homegrown, iterative AI practice rather than simply purchasing a black-box product .

The CPMAI Glossary defines Augmented Intelligence as “enhancing human abilities with AI,” where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .

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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.

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