CPMAI_v7 PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Free Practice Exam Questions (2025 Updated)

Generative AI is defined in the CPMAI Glossary as “AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data.” Using Generative AI for content generation directly addresses the need to produce marketing images automatically.

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In CPMAI’s Trustworthy AI requirements, the Explainable AI layer specifically covers “legal, compliance, and risk considerations [that] might require that the AI system used for decision-making … provide some level of explainability for audit, root cause analysis, or other purposes.” Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result—exactly the focus of the Required AI Explainability Considerations task.

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CPMAI’s Task: AI Pattern Identification requires teams to map their specific business objectives to the most appropriate one or more of the Seven Patterns of AI. Starting from “what are we trying to accomplish?” and then selecting the pattern(s) that align with those goals is the prescribed approach.

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Under CPMAI Phase III: Data Preparation, the Data Format task includes “Data anonymization” as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility.

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During Phase I: Business Understanding, one of the foundational CPMAI tasks is to “determine when to implement automation versus AI,” ensuring that rule-based or non-cognitive alternatives are considered first and AI is only selected when those approaches won’t suffice.

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CPMAI’s Phase II: Data Understanding includes verifying that you have sufficient data volume for each feature to support reliable model training. The learning curve concept underscores that model performance improves with additional training examples. When dimensions are under-represented, the team must source or generate more data—aiming for a minimum number of examples per feature—to avoid underfitting and ensure stable predictions.

In Phase VI: Model Operationalization, CPMAI specifies that batch prediction—“generating predictions offline for large datasets on a scheduled basis”—is the ideal deployment mode when analysis is needed at regular, non-continuous intervals (e.g., weekly) . Continuous methods like real-time or streaming are overkill for periodic reporting.

Large language models (LLMs) excel at generating, understanding, and manipulating text. According to the CPMAI Glossary:

Content summarization is a core NLP function: “the process of using AI/ML techniques to generate a concise overview of a larger body of text.”

Machine translation: “the use of AI to automatically translate text or speech from one language to another.”

Classification: LLMs can assign content to categories via fine-tuned classification heads (“classifier” term), making them suitable for content categorization.

Code generation: As generative AI, LLMs can produce new content, including code snippets, by pattern learning from programming corpora (“generative AI” term).

Search quality improvement: LLMs can rephrase queries, expand keywords, and rank results to enhance search relevance. Though not explicitly detailed in the glossary, this capability derives directly from their generative and understanding strengths.

LLMs are not designed for pure process automation (option A), which is handled by RPA or orchestrators rather than by text-centric models.

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The CPMAI™ v7 methodology groups all data-centric preparation activities—including both data cleansing (“Clean data”) and data augmentation (“Enhance & Augment data”)—into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.

Phase III: Data Preparation is defined in the Workbook’s Table of Contents as covering Data Cleansing & Enhancement tasks (“Clean data” and “Enhance & Augment data”) .

Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists “Task: Clean data” (bringing data quality to modeling-ready levels) and “Task: Enhance & Augment data” (producing derived attributes and new records) as core activities .

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