Free Practice Questions for PMI CPMAI_v7 Exam

The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project’s domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.

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In Phase I: Business Understanding, CPMAI directs teams to “determine business objectives” by engaging stakeholders to surface specific pain points, estimate time-to-ROI, and prioritize projects that deliver tangible business value quickly. Focusing on a narrowly scoped problem with high ROI ensures early success, builds momentum, and validates the AI methodology before tackling larger or more complex initiatives.

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In the Model Operationalization phase (Phase VI), when targeting edge devices, teams must capture the specific constraints around connectivity and on-device performance. The CPMAI Workbook’s Edge Model Data Needs task group instructs project teams to list all requirements and constraints for running models on edge systems—including constraints on external data access—and to determine how models will be deployed locally so they function offline when connectivity is unavailable.

Under “Edge constraints,” the Workbook requires teams to “List constraints on model usage including … requirements for model result response time, constraints on external data access,” underscoring that reliance on a remote API is infeasible without connectivity.

Consequently, the critical operationalization decision is to deploy the model directly on the edge device, ensuring it remains fully functional regardless of internet availability.

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CPMAI’s Model Development phase includes a specialized task—Fine-Tuning / Re-training of Pre-Trained Models—which requires teams to “determine and document what approach will be used to…re-train pre-trained models.” Implementing retraining pipelines ensures the model can be iteratively updated with new data and configurations in a reproducible, automated fashion .

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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 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 .

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 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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The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .

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In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification—mapping business objectives to one or more of the Seven Patterns of AI—so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .