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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 2: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy |
| Topic 3: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
| Topic 5: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
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問題 #255
An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?
答案：D
解題說明：
Compliance with data regulations and explainability standards should be addressed during the business understanding phase so they become formal project requirements from the start. This ensures the AI solution is planned, designed, validated, and operationalized in alignment with legal, ethical, and stakeholder expectations.
問題 #256
You're creating an AI enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Choose all that apply.)
答案：A,B,C,D,G
解題說明：
Data governance for an AI chatbot must address data sharing challenges, privacy risks, security risks, data quality issues, and associated business risks to ensure responsible and compliant data use.
問題 #257
Enhancing and cleaning data is an important action during which phase of CPMAI?
答案：C
解題說明：
Phase II of CPMAI is focused on data preparation, including enhancing and cleaning data to ensure quality input for model training.
問題 #258
An AI project team with a manufacturing company needs to ensure data integrity before moving to model development. They discovered some data inconsistencies due to manual entry errors.
What is an effective method that helps to ensure data integrity?
答案：D
解題說明：
Implementing real-time data validation rules helps prevent manual entry errors from entering the dataset by checking values, formats, ranges, and required fields at the point of entry. This supports data integrity before the team moves into model development.
問題 #259
A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains.
Which method will help identify the qualified data SMEs?
答案：B
解題說明：
PMI-CPMAI distinguishes clearly between different types of expertise needed in an AI project:
AI/ML specialists, data specialists (data SMEs), domain SMEs, and security or infrastructure experts. When the question specifically asks about data subject matter experts (SMEs), the focus is on people who deeply understand how the organization's data is structured, stored, accessed, and governed.
For an AI-powered anomaly detection tool in a government data security context, qualified data SMEs are those who know the existing data architectures, logging systems, data flows, schemas, and constraints. They can explain where relevant data resides (e.g., network logs, access records, system events), how it is currently managed and protected, and what limitations or quality issues may affect AI performance. Evaluating candidates on their expertise with existing data architectures and their ability to optimize databases directly targets this competency.
Knowledge of neural networks, hyperparameter tuning, or GANs is more characteristic of AI/ML engineers, not data SMEs. PMI-CPMAI guidance emphasizes that AI success depends on the right mix of roles, and data SMEs are vital for defining data requirements, ensuring data suitability, and aligning with security and governance standards. Therefore, the method that best identifies the appropriate data SMEs for this anomaly detection project is to evaluate their expertise with current data architectures and their ability to optimize and manage those data systems.
問題 #260
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