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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q180-Q185):

NEW QUESTION # 180
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 Brute force method
C. Use cloud-based technology
D. Use data from a previous project

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 # 181
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be. What should the project manager do?

A. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
B. Move forward without determining which patter of AI this falls under.
C. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
D. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.

Answer: A

Explanation:
Identifying the AI pattern early helps guide data collection and team skill requirements, ensuring the project is aligned with the correct technical approach and business goals.

NEW QUESTION # 182
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project.
What is the best course of action to take with this data?

A. Careful algorithm selection that reduces the need for data.
B. Data integration focused on reducing the number of data sources.
C. Data selection and attribute pruning to reduce overall size and data complexity.
D. Data Deduping to reduce overall size and data complexity.

Answer: C

NEW QUESTION # 183
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

A. Augmented Intelligence
B. RPA is not the right solution to this problem
C. Unattended bot
D. Attended bot

Answer: D

NEW QUESTION # 184
A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
What risk does this create for operationalization?

A. Accumulated technical debt prevents reliable, reproducible deployment
B. The models will not achieve sufficient accuracy
C. The team will be unable to explain model predictions
D. The data will not comply with retention policies

Answer: A

Explanation:
Manual, undocumented, and non-parameterized workflows cannot be reliably reproduced or automated in a production environment. This technical debt must be resolved through pipeline engineering before the model can be dependably operationalized.

NEW QUESTION # 185
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