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CPMAI_v7 Exam Sims - Free PDF PMI First-grade Reliable CPMAI_v7 Exam Vce

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

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

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.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Implement a new data privacy policy
B. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C. Use noise reduction techniques to reduce all forms of data noise
D. Store the data in a data warehouse

Answer: B

Explanation:
Under CPMAI Phase III: Data Preparation, the Data Format task includes "Data anonymization" as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility.
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NEW QUESTION # 71
Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

A. See if simple rules work. If yes, then pick the right AI solution to solve the problem.
B. Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
C. Decide if it's a statistics pattern. If it's statistical then go with the AI project.
D. Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.

Answer: B

Explanation:
The CPMAI Glossary contrasts automation (for deterministic, rule-based tasks) with AI (for probabilistic, learning-based tasks). As a rule of thumb, if a problem exhibits probabilistic patterns that can't be captured by fixed rules, then AI is the appropriate solution; deterministic problems are better handled by simple automation.

NEW QUESTION # 72
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?

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

Answer: E

Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.

NEW QUESTION # 73
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. Everyone on the team should understand the differences between automation and autonomous systems.
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. Senior management should become involved in the project.

Answer: A

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 # 74
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?

A. Model tuning
B. Algorithm Selection
C. Model training
D. Model Selection

Answer: D

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

NEW QUESTION # 75
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