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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Manage AI Model Development and Evaluation	16%	- Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation
Identify Data Needs	26%	- Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure
Operationalize AI Solution	17%	- Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production
Identify Business Needs and Solutions	26%	- Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria
Support Responsible and Trustworthy AI Efforts	15%	- Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q70-Q75):

NEW QUESTION # 70
During development of a healthcare AI diagnostic tool, stakeholders request improving model accuracy by using a complex black-box model, reducing interpretability. Regulatory requirements emphasize transparency in clinical decisions. How should the project manager respond to this request?

A. Evaluate regulatory and business requirements for explainability
B. Accept the change to improve performance
C. Replace stakeholders with technical experts
D. Reject the request immediately

Answer: A

Explanation:
The PM must balance performance with regulatory explainability requirements before making a decision.

NEW QUESTION # 71
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

A. Provide a means to have contestability of the algorithm selected
B. Provide a means to interpret AI results so that cause and effect can be represented.
C. Provide a means to have a different team on the project
D. Provide a means to reverse-engineer the algorithm to inspect its performance

Answer: B

NEW QUESTION # 72
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 needs specific so as long as it's the same pattern then you can reuse the pipeline.
B. There is no issue. Pipelines can be reused as needed between projects
C. Pipelines are model operationalization need specific.
D. Pipelines are pattern and model need specific.

Answer: D

Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.

NEW QUESTION # 73
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. Descriptive Analytics
B. Projective Analytics
C. Diagnostic Analytics
D. Predictive Analytics

Answer: A

Explanation:
Descriptive analytics focuses on summarizing historical data to identify patterns and performance metrics, providing insights into past behaviors.

NEW QUESTION # 74
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?

A. The model would perform more efficiently.
B. The model would generate more creative outputs.
C. The responses would lack relevance.
D. The output would include higher accuracy.

Answer: C

Explanation:
Insufficient context prevents the model from understanding the intended goal, constraints, audience, or required details. As a result, the generated responses are likely to be vague, misaligned, or not relevant to the specific task.

NEW QUESTION # 75
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