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PMI CPMAI_v7 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Domain VI Trustworthy AI: This section is designed for the Project Manager and focuses on ethical, responsible, and transparent AI development. It covers building trustworthy systems, dispelling misconceptions, evaluating real-world ethical concerns, defining responsible frameworks, and implementing mitigation tactics for unintended harms. It addresses data privacy, GDPR compliance, protection of PII, anonymization techniques, security against adversarial threats, and monitoring.
Topic 2
  • CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.
Topic 3
  • Machine Learning: This section is aimed at the Data
  • AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.
Topic 4
  • Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?

Answer: D

Explanation:
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.
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NEW QUESTION # 19
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?

Answer: D

Explanation:
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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NEW QUESTION # 20
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

Answer: B

Explanation:
Under Required AI Explainability Considerations, CPMAI mandates that when a chosen model is a "black- box" with limited native interpretability, teams must implement post-hoc interpretability techniques (e.g., feature#importance plots, surrogate models) to "interpret AI results so that cause and effect can be represented," ensuring stakeholders understand why the model makes its predictions.
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NEW QUESTION # 21
You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?

Answer: A

Explanation:
Generative AI is defined in the CPMAI Glossary as "AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data." Using Generative AI for content generation directly addresses the need to produce marketing images automatically.
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NEW QUESTION # 22
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project.
What is the best course of action to take with this data?

Answer: D

Explanation:
In Phase III: Data Preparation, the Select Data task instructs teams to choose only the records and attributes needed for modeling-documenting inclusions and exclusions to reduce volume and complexity. This selective pruning of columns and rows is the primary mechanism for trimming excessive data before modeling.
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NEW QUESTION # 23
......

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