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| Section | Weight | Objectives |
|---|---|---|
| Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability |
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
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NEW QUESTION # 135
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?
Answer: B
Explanation:
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.
NEW QUESTION # 136
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?
Answer: C
NEW QUESTION # 137
An aerospace company is developing an AI tool for anomaly detection in aircraft engines.
Identifying data sources and their locations is critical. Which method will help to ensure the project meets these objectives?
Answer: C
Explanation:
Establishing a metadata management system helps document where aircraft engine data comes from, where it is stored, what it represents, who owns it, and how it should be used. This supports accurate identification and location tracking of the data sources needed for anomaly detection.
NEW QUESTION # 138
A project manager is evaluating whether an AI model is ready for operationalization. The project team has ensured the model meets accuracy and performance requirements and has conducted extensive testing. However, uncertainty about the model's operationalization in different environments and its compliance with regulatory standards still exists. What should the team do next?
Answer: A
Explanation:
Evaluating the model's adaptability to diverse conditions helps determine whether it can operate reliably across different environments while still meeting deployment, governance, and regulatory expectations.
NEW QUESTION # 139
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy. What is your next best course of action to address this?
Answer: C
Explanation:
Sharing your prior experiences with how your previous team approached AI projects helps educate senior management on the complexities of AI implementation and the importance of doing AI rather than just buying it.
NEW QUESTION # 140
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