Pass the PMI CPMAI CPMAI_v7 Questions and answers with ExamsMirror

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.

CPMAI’s Generic Task Group: Select Modeling Technique in Phase IV: Model Development outlines common cognitive algorithms. For recommendation systems—which rely on finding similar user or item profiles—the K-Nearest Neighbor algorithm is the canonical choice, using customer purchase vectors to locate “nearest neighbors.” In contrast, K-means is purely unsupervised clustering, Neural Networks are more complex and not necessary for basic collaborative filtering, and Hyperpersonalization is an AI pattern, not an algorithm.

=========

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.

Clustering is defined as “an unsupervised process that partitions data into groups (clusters) based on similarity without preassigned labels.” K-Means is the canonical unsupervised clustering algorithm, iteratively assigning points to K centroids to minimize within-cluster variance. This makes K-Means the simplest and most direct choice for grouping data in an anomaly-detection context.

=========

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.

=========

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.

=========

A false positive occurs when the model predicts the positive class (e.g., “tree”) but the actual label is negative (e.g., “flower”). The confusion matrix definition confirms that mislabeling a negative instance as positive maps to the false positive count.

=========

Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers “identifying methods for data labeling and engaging in data labeling efforts,” which is essential for supervised learning workflows like image recognition.

=========

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.

=========

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 .

=========