PMI CPMAI考試資訊 & CPMAI資訊

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PMI CPMAI Exam Syllabus Topics:

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

>> PMI CPMAI考試資訊 <<

CPMAI資訊 - CPMAI認證指南

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最新的 CPMAI CPMAI 免費考試真題 (Q216-Q221):

問題 #216
A project manager is overseeing the development of an AI system and is concerned about maintaining user privacy. The project involves collecting sensitive user data with potential legal and compliance issues to consider.
What should the project manager prioritize to help ensure the AI system adheres to privacy requirements?

A. Implementing a minimalistic data collection policy
B. Developing a comprehensive data anonymization strategy
C. Establishing robust data encryption protocol for privacy
D. Ensuring regular impact assessments are conducted to meet privacy laws

答案：D

解題說明：
Regular privacy impact assessments help identify how sensitive data is collected, processed, stored, and used, while evaluating associated privacy risks against applicable legal and regulatory requirements. This enables the project team to maintain ongoing compliance throughout the AI system's lifecycle.

問題 #217
Accompany wants to determine whether to move forward with an AI project. The project manager has answered and received approval for all questions on technology, data, and business feasibility. Which activity did the project manager perform?

A. Data collection and aggregation
B. Conducted a go/no-go assessment
C. Developed initial machine learning models
D. Operationalized a pilot version

答案：B

解題說明：
A go/no-go assessment evaluates whether the AI project should proceed by confirming technology readiness, data feasibility, and business value. Approval across these areas indicates the project manager completed the decision checkpoint needed to move forward.

問題 #218
A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?

A. Building a unified data warehouse
B. Using an extraction, transformation, and loading (ETL) pipeline
C. Implementing a real-time data processing framework
D. Adopting a federated data model

答案：C

解題說明：
Implementing a real-time data processing framework supports continuous ingestion and integration of current traffic patterns, delivery schedules, and vehicle performance data. This enables the route optimization system to use timely, diverse operational data for accurate decision-making.

問題 #219
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?

A. Model discovery
B. Data operations
C. Model Governance
D. Model operations

答案：C

問題 #220
Your team is running a simulation-based optimization exercise to increase routing efficiency.
Learning for this exercise is done through "trial and error" Which type of machine learning approach is being leveraged for this exercise?

A. Supervised Learning
B. Reinforcement Learning
C. Unsupervised Learning
D. All would work equally well

答案：B

解題說明：
Reinforcement learning involves learning optimal actions through trial and error by interacting with the environment, making it suitable for simulation-based optimization.

問題 #221
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