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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Identify Data Needs	26%	- Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure
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 - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks
Manage AI Model Development and Evaluation	16%	- Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability
Operationalize AI Solution	17%	- Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q90-Q95):

NEW QUESTION # 90
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?

A. Iterative modeling and evaluation
B. Automatic algorithm selection
C. Automatic model assessment
D. Automatic hyperparameter tuning
E. Automatic model selection

Answer: E

NEW QUESTION # 91
A financial institution is planning to use AI capabilities to detect fraudulent transactions. The project manager needs to ensure that all necessary requirements are met before proceeding.
What is a necessary initial task?

A. Identifying the primary stakeholders and their needs
B. Assessing the ethical implications of using AI for fraud detection
C. Determining the scalability of AI solutions for transaction monitoring
D. Evaluating the accuracy of current fraud detection methods

Answer: A

Explanation:
Identifying the primary stakeholders and their needs is a necessary initial task because the project manager must understand the business, regulatory, operational, security, and user requirements before determining whether the AI fraud detection solution should proceed.

NEW QUESTION # 92
Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?

A. They did not have data governance in place
B. They did not practice model versioning and keep all versions of the model
C. They did not have a model retraining pipeline that took into account models
D. They did not practice model iteration and properly iterate on the model

Answer: B

NEW QUESTION # 93
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 Negative results
B. True Negative results
C. False Positive results
D. True Positive results

Answer: C

NEW QUESTION # 94
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects.
What's the next best course of action?

A. Hire an entirely new team making sure there is at least one data scientist on this new team
B. Bring in data centric methodology best practices to get this project back on track
C. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
D. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices

Answer: B

NEW QUESTION # 95
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