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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

Data for AI: This domain targets the Data
AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.

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

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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?

A. An incomplete milestone list
B. Lack of stakeholder support
C. Data Drift
D. Model Drift

Answer: C

Explanation:
A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
=========

NEW QUESTION # 41
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind. At what phase of CPMAI would Trustworthy AI be considered?

A. Phase IV
B. All phases
C. Phase V
D. Phase III
E. Phase II
F. None of the phases
G. Phase I
H. Phase VI

Answer: B

Explanation:
Trustworthy AI is not confined to a single phase but is woven throughout the entire CPMAI lifecycle:
The CPMAI Exam Content Outline under Domain VI: Trustworthy AI specifies tasks such as "Apply ethical AI concepts throughout the development lifecycle," "Ensure compliance with privacy/security requirements," and "Implement transparency and explainability" at every stage .
The CPMAI Workbook's Task Group: Trustworthy AI Requirements (covering transparency, explainability, ethics, compliance, and responsible-AI frameworks) appears as an overarching set of artifacts and considerations that map back to multiple phases-beginning with Business Understanding and continuing through Model Operationalization .
Thus, Trustworthy AI considerations apply across all CPMAI phases.

NEW QUESTION # 42
You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?

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

Answer: F

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

NEW QUESTION # 43
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?

A. Use cloud-based technology
B. Use or extend a pre-trained model
C. Use brute force method
D. Use data from a previous project

Answer: B

Explanation:
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.

NEW QUESTION # 44
Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?

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

Answer: D

Explanation:
The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .
=========

NEW QUESTION # 45
......

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