CPMAI_v7 Cognitive Project Management in AI CPMAI v7 - Training & Certification Questions and Answers

Phase V: Model Evaluation is dedicated to validating a trained model’s performance against technical metrics and the business success criteria defined earlier. This phase encompasses tasks such as generating performance results, measuring KPIs, and deciding whether to retrain or proceed to operationalization.

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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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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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In the CPMAI Glossary, attended bots are defined as “software automation tools that work alongside humans (typically in front-office roles) to assist with tasks and boost productivity.” Call-center assistance is a classic front-office scenario requiring a bot that human agents can invoke interactively.

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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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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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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.

Generative AI encompasses techniques that create new data based on learned patterns. Outpainting is the specific generative approach for extending an image’s content beyond its original borders. It leverages the same learned representations as inpainting or super‐resolution but focuses on generating entirely new regions around an existing image.

The CPMAI methodology’s Model Iteration Approach (Phase V) explicitly calls out that “models will need continuous iteration, especially if they are only marginally providing the desired results” and requires teams to “detail approach that will be used to iterate this model to improve on any of the results in this Phase” . Failing to include a model retraining pipeline means the model cannot adapt to new data distributions, leading to performance degradation over time.

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In CPMAI’s Executing the Business Understanding Phase, teams first “formulate AI-specific business questions” and “estimate time-to-ROI for various AI project types” before matching business needs to cognitive patterns . This ensures ROI-driven objectives guide the selection of one or more of the Seven Patterns of AI, rather than the reverse.

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In Phase II: Data Understanding, the Pre-Trained and Third-Party Model Usage task directs teams to “determine if team will use a model…developed by another team, or by a third-party organization” and to gather information about “costs to use model, origin of data and methods of training, performance measures, and other factors that will determine appropriateness of model” before proceeding . When in-house data is insufficient, leveraging existing third-party models is the recommended path to avoid project stall.

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When the goal is to group or organize unlabeled data into meaningful categories, CPMAI specifies the use of unsupervised clustering algorithms—for example, “if it is determined that an unsupervised clustering algorithm such as K-means will be used, the tool should support such needs” . Clustering is the appropriate method for discovering groupings by type.

In Phase I’s AI Skills Assessment, CPMAI directs teams to “List the cognitive skills you have available” and to identify “What expertise and skills you have available to you that you can use for this project” as well as any skills gaps to address . The methodology—and the CPMAI Glossary’s definition of a data scientist—emphasizes core competencies in:

Data Engineering & Preparation (manipulating, collecting, transforming large data sets)

Critical Thinking (interpreting insights to align with business goals)

Algorithmic Understanding (selecting and applying the right statistical or ML models)

Mathematical Proficiency (especially statistics and calculus underpinning model creation)

By contrast, prompt engineering (A) is a specialized role for LLM interactions, not a general data-science core competency; and RPA-centric automation skills (E) fall outside the CPMAI focus on cognitive/ML capabilities.

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Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines—including training and inference pipelines—alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots.

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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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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CPMAI’s Differentiate AI Project Management Approaches task stresses that effective AI adoption requires building internal capabilities and understanding domain-specific challenges. By sharing your own team’s past experiences—how you diagnosed the problem, structured the data, and developed AI solutions—you guide leadership toward establishing a homegrown, iterative AI practice rather than simply purchasing a black-box product .

CPMAI’s Phase III: Data Preparation is a distinct phase that encompasses data cleansing, augmentation, labeling, and pipeline construction. Because data engineering often accounts for the majority of AI project effort, omitting this phase from initial estimates leads to significant timeline underestimation. Project timelines must explicitly include Phase III activities to be realistic .

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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.

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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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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.

The Phase IV: Model Development generic tasks include:

Select Modeling Technique (algorithm selection)

Generate model test design

Model Training / Model Building

Hyperparameter Optimization (model tuning)

Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .

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The CPMAI Glossary defines Augmented Intelligence as “enhancing human abilities with AI,” where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .

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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.

Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .