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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Support Responsible and Trustworthy AI Efforts	15%	- Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks
Topic 2: Identify Business Needs and Solutions	26%	- Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions
Topic 3: Manage AI Model Development and Evaluation	16%	- Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation
Topic 4: Operationalize AI Solution	17%	- Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch
Topic 5: Identify Data Needs	26%	- Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q244-Q249):

NEW QUESTION # 244
An organization aims to improve transparency and trust in its AI systems. Stakeholders request documentation outlining model purpose, limitations, risks, and performance characteristics. Which artifact should the project manager ensure is created and maintained?

A. Deployment script
B. Model card
C. Data pipeline diagram
D. Source code repository

Answer: B

Explanation:
Model cards provide transparency and communicate risks and limitations.

NEW QUESTION # 245
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 or extend a pre-trained model
B. Use data from a previous project
C. Use cloud-based technology
D. Use Brute force method

Answer: A

Explanation:
Using or extending a pre-trained model (transfer learning) significantly speeds up training by leveraging existing learned features, reducing the need to train from scratch.

NEW QUESTION # 246
During the business understanding phase, the project manager realizes that the project team has overlooked requirements for the AI model. The model should have a high level of explainability and transparency. What should the project manager do?

A. Develop an algorithm to account for future model adjustments.
B. Revisit the training data for the smart algorithm.
C. Ensure data compliance requirements are clearly documented.
D. Define a process for data aggregation and analysis.

Answer: C

Explanation:
Clearly documenting compliance requirements helps ensure explainability and transparency expectations are formally captured during business understanding. This guides later model selection, validation, governance, and stakeholder review so the AI system can meet accountability and regulatory expectations.

NEW QUESTION # 247
A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low.
What is the cause for the precision issue?

A. The model is overfitting the training data
B. The feature selection process is flawed
C. The model is underfitting the validation data
D. The training data is unbalanced

Answer: D

NEW QUESTION # 248
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:

A. False Positive results
B. True Positive results
C. True Negative results
D. False Negative results

Answer: A

Explanation:
Incorrectly categorizing flowers as trees represents false positives - instances where the model incorrectly predicts the positive class when it should not.

NEW QUESTION # 249
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