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PMI CPMAI Exam Syllabus Topics:

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

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最新的 CPMAI CPMAI 免費考試真題 (Q149-Q154):

問題 #149
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Implement a new data privacy policy
B. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C. Use noise reduction techniques to reduce all forms of data noise
D. Store the data in a data warehouse

答案：B

問題 #150
Clean, well-labeled datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Use the same data for all sets
B. Split by random subsampling
C. Split by patterned subsampling
D. Split by alphabetical order

答案：B

問題 #151
A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address the risks related to data accuracy, privacy, and misuse.
What represents the highest risk to the agency?

A. The user data is stored in an unsecured database.
B. The AI system relies on third-party providers.
C. The system lacks a transparency process.
D. The AI system is not regularly updated with new data.

答案：A

解題說明：
Storing user data in an unsecured database creates the highest risk because it directly exposes sensitive information to unauthorized access, privacy violations, misuse, and regulatory noncompliance. For a government AI system, weak data security can undermine public trust and create serious legal and ethical consequences.

問題 #152
An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?

A. Number of features delivered
B. User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
C. Response time only
D. Lines of code written

答案：B

解題說明：
PMI-CPMAI emphasizes establishing acceptable performance metrics and aligning AI outcomes to business value while ensuring responsible and trustworthy practices. For chatbots, business value includes deflection/containment (how many issues are resolved without human agents), customer experience (satisfaction), and operational performance (latency). Risk measures must also be included because trustworthy AI requires governance and compliance controls (privacy/security, transparency, accountability). Therefore, metrics that combine outcomes and controls--user satisfaction, containment, correct escalation/hand-off, and privacy/compliance incident rates--are the most PMI-aligned set.

問題 #153
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Choose all that apply.)

A. Content Generation
B. Human Augmentation
C. Programmatic automated content generation
D. Data Augmentation for Training
E. Virtual Avatars and Characters
F. Explainable Decision-support systems

答案：A,B,C,D,E

解題說明：
Generative AI is well-suited for augmenting human tasks, creating content, augmenting training data, automating content generation programmatically, and powering virtual avatars. Explainable decision-support typically involves different AI techniques focused on transparency rather than generation.

問題 #154
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