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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q234-Q239):

NEW QUESTION # 234
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

A. Governed AI
B. Responsible AI
C. Transparent AI
D. Ethical AI
E. Explainable AI

Answer: C

Explanation:
Transparent AI includes mechanisms that allow users to understand and contest AI decisions, ensuring openness and accountability in the system.

NEW QUESTION # 235
A company is operationalizing an AI system for automated customer service. The system appears to favor queries in one language over others, despite having been trained on a multilingual dataset. Which two issues should the project lead investigate? (Choose two.)

A. Presume the dataset is at fault, without conducting a detailed analysis.
B. Evaluate the system's response times for different languages.
C. Examine whether the preprocessing techniques varied across languages.
D. Check the distribution of training samples per language.
E. Determine whether the neural network architecture supports multilingual processing.

Answer: C,D

Explanation:
Uneven preprocessing can cause one language to be cleaned, tokenized, normalized, or encoded differently from others, leading to biased performance. Checking the distribution of training samples per language helps determine whether the model had enough balanced representation to learn each language effectively.

NEW QUESTION # 236
A project manager is evaluating an AI model's performance to determine its business impact. The model has shown high accuracy during testing but has not been implemented yet in a live environment. The project manager needs to decide what to assess to ensure the model's effectiveness. What should the project manager do?

A. Check if the training data represents real-world scenarios.
B. Conduct a cost-benefit analysis of the model's implementation.
C. Evaluate the ethical implications of operationalizing the model.
D. Determine the scalability for future expansions.

Answer: A

Explanation:
Checking whether the training data represents real-world scenarios helps determine if the model's high testing accuracy is likely to translate into effective performance after deployment. A model can test well but fail to deliver business impact if the data used for training and testing does not reflect live operating conditions.

NEW QUESTION # 237
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 # 238
A sponsor asks the project manager to commit to a fixed delivery date and a specific model accuracy figure at project initiation.
How should the project manager respond?

A. Explain that achievable accuracy cannot be known before data and modeling work, and propose phase-gate commitments
B. Commit to both and manage variance through change control
C. Decline to provide estimates until the model is trained
D. Commit to the date and treat accuracy as a stretch goal

Answer: A

Explanation:
Achievable model performance depends on data quality and characteristics that are unknown at initiation. Committing to phase-level outcomes with go/no-go decision points gives the sponsor control and visibility without requiring an unsupportable guarantee.

NEW QUESTION # 239
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