CPMAI_v7 PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Free Practice Exam Questions (2025 Updated)

In Phase IV’s Usage of AutoML task, CPMAI expressly recommends leveraging automated machine-learning tools to accelerate model creation when specialized expertise or time is limited. Documenting how AutoML will generate, evaluate, and export models allows teams to maintain pace without sacrificing rigor.

CPMAI defines a Data Scientist as the role responsible for “formulating data-driven hypotheses, selecting and applying statistical algorithms, interpreting model results, and communicating insights to stakeholders,” all of which require critical thinking under uncertainty, advanced mathematics, and strong data-visualization skills .

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In CPMAI™ v7’s Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities—including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This “augmentation” is described as “constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes” .

Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine “What transformation or multiplication activities can be done to increase training data volume while maintaining quality” . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.

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Synthetic data is “artificially generated data that mimics real-world data, used when actual data is scarce or sensitive.” Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead.

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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.

Phase II: Data Understanding centers on identifying just the right amount of data for model training—neither too little (risking underfitting) nor too much (wasting resources and introducing noise). This balanced—“Goldilocks”—approach ensures you collect sufficient high-quality, relevant records to meet cognitive objectives without incurring unnecessary cost or complexity.

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CPMAI emphasizes that data is only as valuable as the team’s ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an “AI Skills Assessment,” which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.

The Workbook’s Task Group: Assess Situation in Phase I explicitly calls out “AI Skills Assessment” alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.

Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can “apply Big Data approaches to enhance AI capabilities”, which presupposes the presence of experienced data professionals.

Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.

Continuous System monitoring (C): Phase VI’s “Monitoring and maintenance plan” requires teams to define “What continuous monitoring and management approach and tools will be used for the model in this iteration?” to ensure the model continues to provide expected results in operation .

Data source identification (D): In Phase II: Data Understanding, teams must “Describe Data,” including “Data source formats” and “Training data identification,” to maintain visibility into where the model’s inputs originate—essential for governance and troubleshooting .

Human chain of accountability (F): The “Model Governance Framework” task directs project teams to document “Determination of Governance Team,” identifying members who will serve as the “owners” of the model and be responsible for its usage, soliciting feedback, and addressing concerns—establishing a clear accountability structure .

Key Performance Indicators (KPIs) (G): Domain V’s “KPI Measurement” task mandates that teams “Align model performance with business key performance indicators” and implement ongoing KPI evaluation as part of quality assurance, providing the metrics by which governance bodies assess model health and business impact .

Options A, B, E, and H fall outside the core ongoing governance activities defined in CPMAI v7. Continuous monitoring of deployed models, clear data lineage, defined human accountability, and KPI tracking are the pillars of robust AI governance.

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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.

Tom Mitchell’s widely adopted formulation captures ML’s essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI’s view that ML enables systems to learn from data and improve over time (“The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.”) .

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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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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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CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I’s Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI—ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .

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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.

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.

Naive Bayes classifiers are a family of “simple probabilistic classifiers based on Bayes’ theorem with the ‘naive’ assumption of feature independence,” making them fast to train and easy to interpret—ideal for teams new to AI tackling binary tasks like spam detection .

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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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Transfer learning is defined as a technique that “leverages a pretrained model (trained on a large, relevant data set) as a starting point for a new, related task,” allowing you to reuse learned representations (e.g., convolutional layers trained on one image domain) when training on another domain (like cars → animals) .

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