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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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 4

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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
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?

A. Generative AI solutions for content generation
B. Image and object detection and recognition systems
C. Decision tree and Random Forest approaches
D. Autonomous patterns and process automation

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.
=========

NEW QUESTION # 31
When building your model you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

A. Phase I
B. Phase VI
C. Phase II
D. Phase V
E. Phase III
F. Phase IV

Answer: D

Explanation:
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.
=========

NEW QUESTION # 32
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

A. Predictive Analytics
B. Descriptive Analytics
C. Projective Analytics
D. Diagnostic Analytics

Answer: B

Explanation:
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.
=========

NEW QUESTION # 33
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A. Senior management should become involved in the project.
B. Nothing - running automation projects like autonomous projects is the correct thing to do.
C. Applying a hybrid approach of automation and AI best practices would have achieved better results.
D. Everyone on the team should understand the differences between automation and autonomous systems.

Answer: D

Explanation:
During Phase I's Cognitive Project Requirements tasks, CPMAI instructs teams to "Determine when to implement automation versus AI." Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.
=========

NEW QUESTION # 34
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project.
What's wrong with this suggestion?

A. Pipelines are pattern and model need specific.
B. Pipelines are model operationalization need specific.
C. There is no issue. Pipelines can be reused as needed between projects.
D. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.

Answer: A

Explanation:
In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.
=========

NEW QUESTION # 35
......

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