PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification CPMAI_v7 Exam Dumps: Updated Questions & Answers (November 2025)

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.

This reaction is a classic example of the Uncanny Valley phenomenon, where a nearly human-like robot triggers discomfort or eeriness in users because it sits in the valley between clearly robotic and convincingly human appearances. Although not explicitly named in the CPMAI glossary, addressing this user experience concern falls under Continuous Improvement and Respect for People, ensuring cognitive solutions are designed for positive user acceptance.

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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 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 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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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.

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CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams “create a reusable data pipeline to collect, ingest, and prepare data for training” and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists. While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.

Under Domain VI: Trustworthy AI in the CPMAI Exam Content Outline, Responsible AI covers establishing policies, guidelines, and governance that ensure AI solutions are developed for positive, ethical use and prevent misuse. Defining proper-use guidelines and embedding ethical intent into facial recognition directly align with Responsible AI practices .

CPMAI defines real-time prediction as “the generation of predictions instantly as new data is received, which is crucial for time-sensitive applications” and positions it as the go-to approach when models must deliver immediate outputs for continuous monitoring scenarios . While streaming and microservices are architectural styles or ongoing learning frameworks, real-time prediction speaks directly to the requirement of instant inference.

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Under Domain VI: Trustworthy AI – Task 2: Implementing AI Privacy and Security, CPMAI mandates that teams must:

Apply data privacy principles and “ensure compliance with General Data Protection Regulation (GDPR)” and other relevant laws regardless of location .

Identify and protect Personally Identifiable Information (PII) and “develop comprehensive AI safety and security protocols,” which encompasses securing both model data and metadata and enforcing security monitoring for production systems .

Implement best practices for data anonymization, defense against adversarial attacks, and the secure handling of datasets—this includes securing data at rest and securely storing training data in accordance with organizational and regulatory requirements .

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