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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: CPMAI Methodology	41%	- Phase II: Data Identification & Curation 1. Identify data needs 2. Assess data readiness and quality - Phase VI: Iteration & Monitoring 1. Retraining and model drift management 2. Continuous monitoring and iteration - Phase III: Algorithm Selection & Model Training 1. Oversee AI/ML model technique selection 2. Manage model training and configuration - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions - Phase IV: Model Evaluation 1. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics
Topic 2: Managing AI	8%	- Managing AI project teams and resources - Stakeholder management - Risk management in AI projects
Topic 3: AI Fundamentals	16%	- Types of AI and Machine Learning - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence
Topic 4: Trustworthy AI	9%	- Transparency and explainability - Privacy and security - Ethical considerations and bias
Topic 5: Data for AI	13%	- Data preparation and preprocessing - Data strategy and governance - DataOps concepts
Topic 6: Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision)

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q184-Q189):

NEW QUESTION # 184
An aerospace firm is developing an AI system for predictive maintenance of their aircraft. The project team needs to define the required data to train the model. Which activity should the project manager implement?

A. Implementing data cleaning and preprocessing routines
B. Setting up real-time data streaming from aircraft sensors
C. Conducting a pilot test with a small dataset
D. Developing a comprehensive data collection strategy

Answer: D

Explanation:
Developing a comprehensive data collection strategy defines what aircraft sensor, maintenance, operational, failure, and environmental data is required to train the predictive maintenance model.
This ensures the model is built from relevant and sufficient data before later preparation, streaming, or testing activities begin.

NEW QUESTION # 185
When building your model you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

A. Phase V
B. Phase I
C. Phase VI
D. Phase II
E. Phase IV
F. Phase III

Answer: A

NEW QUESTION # 186
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. Requirements were not documented in adequate detail
D. The team lacked sufficient computing resources

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 # 187
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

A. Supporting training data
B. Bias-variance tradeoff
C. Hyperparameter settings
D. The algorithm code

Answer: A,C,D

NEW QUESTION # 188
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, your team doesn't have access to all the data it needs. What's the best course of action?

A. Move ahead as planned so you stay on time with your project.
B. Cautiously move forward knowing you may need to pause mid-project which is ok.
C. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
D. Do not move forward until you have access to all the data you need.

Answer: B

Explanation:
Moving forward cautiously while recognizing potential pauses allows progress with flexibility, enabling the team to adapt when data access issues arise without derailing the entire project.

NEW QUESTION # 189
......

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