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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Topic 2: Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
Topic 3: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection
Topic 4: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Define AI business objectives 2. Feasibility and ROI analysis
Topic 5: Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
Topic 6: AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q100-Q105):

NEW QUESTION # 100
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

Explanation:
Unbalanced training data can cause the model to favor the majority class and produce more false positives for the target outcome. This lowers precision because fewer of the model's positive predictions are actually correct.

NEW QUESTION # 101
For AI projects the code and systems don't matter as much as the dat

A. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?
B. Understanding which algorithms are best for your data needs.
C. Collect enormous amounts of data - the more data the better.
D. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
E. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.

Answer: C

NEW QUESTION # 102
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?

A. Data augmentation strategies
B. Exploratory data analysis (EDA)
C. Detailed cost-benefit analysis
D. Advanced data labeling methods

Answer: B

Explanation:
Exploratory data analysis helps verify whether the server downtime data is complete, accurate, consistent, relevant, and suitable before moving into data preparation. It allows the project manager to identify missing values, outliers, patterns, anomalies, and quality issues that could affect model development.

NEW QUESTION # 103
Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?

A. Team members should not be able to move to new projects until senior management signs off
B. Model Evaluation requires continuous model evaluation, retraining, and operationalization
C. You need to make sure the AI Go/No Go questions have been addressed
D. Nothing is wrong with this workflow. You can move to the next iteration

Answer: C

NEW QUESTION # 104
An organization has been running an AI project using a traditional waterfall approach. After nine months, the delivered model performs poorly against real-world data, and stakeholders are dissatisfied.
What is the primary methodological cause of this outcome?

A. The data science team was not co-located with business stakeholders
B. Feedback and validation were deferred until the end of the lifecycle
C. The team lacked sufficient computing resources
D. Requirements were not documented in adequate detail

Answer: B

Explanation:
AI projects are inherently experimental and data-dependent, so assumptions must be validated continuously. A waterfall approach defers learning until delivery, allowing incorrect assumptions about data and model behavior to persist for months before they are discovered.

NEW QUESTION # 105
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