Pass the PMI CPMAI CPMAI_v7 Questions and answers with CertsForce

Descriptive analytics focuses on summarizing and reporting on past data—exactly what’s needed to highlight historical purchasing patterns and performance metrics. The CPMAI Glossary defines analytics as “the use of statistical and computational methods to extract meaningful insights from data (related: predictive, descriptive, …)” where descriptive analytics provides the retrospective view.

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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.

Phase V: Model Evaluation is where you validate not only technical performance but also alignment with the business success criteria defined in Phase I. Within this phase, the KPI Measurement task focuses on “measuring and evaluating the model against Phase I objectives,” ensuring the solution meets its intended business outcomes before moving forward.

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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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K-nearest neighbor (KNN) is a classic supervised classification algorithm that assigns a class label based on the majority label of the K closest training samples in feature space. In contrast, Gaussian Mixture Models and K-means are unsupervised clustering techniques, and Q-learning is a reinforcement-learning algorithm—not a supervised classifier .

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The process of reducing a dataset’s feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t‐distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.

CPMAI’s glossary defines data splitting as “dividing a data set into subsets (e.g., training, validation, test) for model development and evaluation,” typically achieved via random subsampling to ensure each subset is representative of the underlying distribution and to prevent sampling bias.

A sudden shift in the incoming data distribution—such as seasonal changes in customer behavior—is known as data drift. CPMAI defines model drift as “degradation in a model’s performance over time as the underlying data distribution changes,” implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .

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In Phase II: Data Understanding, CPMAI’s Generic Task Groups focus on:

Collecting initial data (identifying sources and volumes) and describing data (location/source) .

Verifying data quality to ensure completeness and correctness .

Team composition (the makeup of your data team) is addressed earlier under Phase I: Assess Situation, not during the Data Understanding phase where data requirements (quality, volume, source) are determined.

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The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.

A CPMAI Glossary self-test question states that Predictive Analytics “uses historical data to forecast future outcomes” .

Another glossary question defines predictive analytics as aiming “to use historical data to forecast future outcomes” .

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