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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q145-Q150):

NEW QUESTION # 145
An IT services company is developing an AI system to automate network security monitoring. The project manager needs to consider various factors to mitigate risks associated with false positives and false negatives. Which action should the project manager implement?

A. Operationalizing the nearest neighbor detection algorithms
B. Conducting model combinations and trade offs
C. Establishing a continuous feedback loop with security
D. Implementing a robust data security validation process

Answer: C

Explanation:
Establishing a continuous feedback loop with security teams helps validate alerts, refine model behavior, and adjust detection rules or thresholds based on real-world monitoring outcomes. This directly reduces the impact of false positives and false negatives in network security operations.

NEW QUESTION # 146
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?

A. Using an explainability framework
B. Conducting a stakeholder impact analysis
C. Implementing a data encryption protocol
D. Performing continuous monitoring and auditing

Answer: D

Explanation:
PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one- time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI-CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation--foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes.

NEW QUESTION # 147
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. Data Deduping to reduce overall size and data complexity.
B. Data selection and attribute pruning to reduce overall size and data complexity.
C. Careful algorithm selection that reduces the need for data.
D. Data integration focused on reducing the number of data sources.

Answer: B

Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.

NEW QUESTION # 148
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?

A. Make sure the model lives on the edge device so it can be used regardless of internet connection
B. Make sure the model is available over a cloud-based API
C. Make sure that you can use Generative AI solutions on an edge device
D. Make sure the model lives in a hybrid environment

Answer: A

Explanation:
For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.

NEW QUESTION # 149
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled. Which phase of CPMAI is this done?

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

Answer: E

Explanation:
Phase II of CPMAI covers data preparation activities, including data labeling, to ensure the dataset is ready for model training.

NEW QUESTION # 150
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