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

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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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.

Algorithm selection/code must be documented under the Select Modeling Technique task, where teams “document the actual algorithm/modeling technique to be used” .

Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates “create a reusable data pipeline to collect, ingest, and prepare data for training purposes” .

Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams “list the final, optimized settings” used for model building .

The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.

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The Continuous Improvement and Respect for People principle in CPMAI stresses involving end users early—gathering their pain points (A), clarifying that AI will augment rather than replace roles (B & C), and providing thorough training to build confidence (D). Engaging stakeholders throughout the project lifecycle and prioritizing user-centered design are key to adoption.

In Phase II: Data Understanding, CPMAI’s Data Selection tasks require teams to “Decide on the data to be used for analysis” by first listing all available sources and then selecting only those records and attributes that meet quality and relevance criteria . Taking a company-wide data inventory ensures you don’t overlook relevant datasets before narrowing down to what truly applies.

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In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.

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