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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q321-Q326):

NEW QUESTION # 321
An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?

A. Understand the data characteristics.
B. Delete the suspicious data manually.
C. Create a data visualization.
D. Evaluate the data freshness and relevance.

Answer: A

Explanation:
Understanding the data characteristics is the first step because the project manager must determine how the data is structured, sourced, aggregated, formatted, and related before identifying why inaccuracies may occur. This provides the foundation for evaluating freshness, relevance, cleansing needs, and aggregation risks.

NEW QUESTION # 322
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 training data is unbalanced
C. The feature selection process is flawed
D. The model is underfitting the validation data

Answer: B

NEW QUESTION # 323
A telecommunications company is preparing data for an AI tool. The project team needs to ensure the data is in the right shape and format for model training. In addition, they are working with a mix of structured and unstructured data. Which method will address the project team's objectives?

A. Converting unstructured data into structured formats
B. Using a hybrid storage system for both data types
C. Separating structured and unstructured data into different databases
D. Employing a data transformation tool to standardize formats

Answer: D

Explanation:
Employing a data transformation tool helps standardize structured and unstructured data into consistent shapes, formats, and representations that can be used effectively for model training.

NEW QUESTION # 324
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. There is no issue. Pipelines can be reused as needed between projects
B. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.
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 # 325
A manufacturing firm plans to use AI to predict equipment failures. The team can access sensor data but it contains many missing values and out-of-range readings. What should the project manager prioritize first?

A. Data understanding and quality assessment to characterize missingness and anomalies
B. Focus only on UI design for the dashboard
C. Ignore the sensor data and use only expert opinion
D. Deploy the model quickly and fix issues later

Answer: A

Explanation:
PMI-CPMAI stresses that AI delivery is data-driven and iterative, and that teams must manage the Data Understanding work to identify appropriate datasets and validate quality before model development. Missing values and out-of-range readings can materially distort training and inference, so the PMI-aligned priority is to characterize the data: understand sources, sampling frequency, sensor health, definitions, and the nature of missingness (random vs. systematic), then define cleansing/imputation and anomaly-handling strategies as part of data preparation.

NEW QUESTION # 326
......

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